{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/4_real_world_data_analysis_and_preparation/1_exploratory_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1a5StKeSfpXc"
   },
   "source": [
    "<a name='0'></a>\n",
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j07Qj7tkf6x8"
   },
   "source": [
    "EDA is an important step in ML project. This is where we get to dig into the data, trying to understand more about it. \n",
    "\n",
    "By performing EDA, we can:\n",
    "* Know the summary statistics\n",
    "* Spot if there are missing values and the appropriate strategy for handling it\n",
    "* Spot if the data is skewed (or imbalanced)\n",
    "* Correlation between features\n",
    "* Understand the important features (or attributes) worth keeping and unhelpful features \n",
    "* Above all, answer some of the pressing questions about the data. These questions can be specific to data and its features but will around things like why this and this, or what could have caused this and this based on the analysis, etc...There are no right or wrong questions, the idea here is to use data to see if we can help answer some questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOpMshW0ibUC"
   },
   "source": [
    "### Real World Data Exploratory Analysis : Titanic\n",
    "\n",
    "In this lab, we will use Titanic dataset which is available on Kaggle. We can also load it from Seaborn datasets but we will use the real data from [Kaggle](https://www.kaggle.com/c/titanic/data).\n",
    "\n",
    "Contents: \n",
    "\n",
    "* [1. Imports](#1)\n",
    "* [2.  Loading the dataset](#2)\n",
    "* [3. Quick look into the dataset](#3)\n",
    "* [4. Summary statistics](#4)\n",
    "* [5. Basic information](#5)\n",
    "* [6. Missing data](#6)\n",
    "* [7. More analysis](#7)\n",
    "* [8. Checking feature correlations](#8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euIU3dDdnqBx"
   },
   "source": [
    "<a name='1'></a>\n",
    "\n",
    "## 1.   Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "5xEWDVlantEf"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A1drWczAn-gU"
   },
   "source": [
    "<a name='2'></a>\n",
    "    \n",
    "## 2. Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "DFyZXebpn5V8"
   },
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "quWkehuIbKkX"
   },
   "source": [
    "If the data was coming from external sources, say `csv`, we could have used Pandas to read the file.\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "titanic = pd.read_csv('.....')\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WdcgX1tlo6KT"
   },
   "source": [
    "<a name='3'></a>\n",
    "## 3. Quick Look into the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "24wB4sLbovOF",
    "outputId": "5a11fccf-2532-4cdf-a011-6fbf74a3e8df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QseC2t13oJM4"
   },
   "source": [
    "By default, titanic.head() will display the first five rows. But you can display the rows you want, just pass the integer. Example: `titanic.head(10)`. .`tail() will display the last rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
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     "base_uri": "https://localhost:8080/",
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    "id": "vI-c8NwUoMgl",
    "outputId": "432bdc4e-46ac-4ed1-f5d5-140fb3ccb608"
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
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     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
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     "base_uri": "https://localhost:8080/",
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    },
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    "outputId": "b687441e-0513-48e7-f143-a8d224237f26"
   },
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       "      <td>32.0</td>\n",
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       "      <td>7.75</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
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       "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.00        S   First   \n",
       "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.00        C   First   \n",
       "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
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     "execution_count": 66,
     "metadata": {},
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    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "X2m8EnjppGff"
   },
   "source": [
    "<a name='4'></a>\n",
    "## 4. Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "mFzTivyCpJQG",
    "outputId": "17c24b28-aad6-4644-8bb9-52b7e44ef637"
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "5qY8-VWopKxk",
    "outputId": "86f60aa5-0cd8-481f-dfcc-bb5feca2f70c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std   min      25%      50%   75%       max\n",
       "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
       "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
       "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
       "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
       "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
       "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P9lIkjoqfCe5"
   },
   "source": [
    "<a name='5'></a>\n",
    "## 5. Basic Information\n",
    "\n",
    "1. How many people who survived and died from the titanic crash? Can you use the appropriate visualization to show these people?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eq0AYIDQfJLo",
    "outputId": "8f24b494-ab45-42b0-f32c-907d958beaa7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "BPGSWsLef0qs",
    "outputId": "eaee68e8-80e2-4083-85e2-e3532f3cf3f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vs4y/DHhVa58FXJbkacCLgI8muQH4c+DINubFwEda+5K5fhlpby0ZdwHSQlBV/5bkeOA04Pwkm4AdPPI/Tofsssm3Zmy7Lcm9SZ7H9B/+X5vlIzYCf5DkCOCFwNXAU4EHqur5uytrb7+PtLecKUhAkmcBD1fVXwLvBo4H7mD6DzjALz/OLi4D3gIcWlU37rqyqh5i+om0FwCfqKrvVtU3gP9IckarIUmOa5t8jukZBcBr9vqLSXNkKEjTngt8oR3GeStwPvB24IIkk8B3H2f7K5j+I375HsZcBry2ve/0GmBtki8Dt/DIT6G+ATg3yU34S3iaR16SKknqnClIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6v4fThe6X4oYEPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic, x='survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xZXH_0d6gaeq"
   },
   "source": [
    "2. How many `pclass` are there in the dataset? I it the same as the the text column `class`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sLVwhemWgu3Y",
    "outputId": "b7b719bd-b6f0-4208-bd52-52572e08655f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nOdUnIuigzvN",
    "outputId": "fad553c5-0e30-4371-d7e1-483e84247d32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "hjgXs2qRi3dj",
    "outputId": "2f65a2dc-05bd-4aa8-9dcd-186c4621fa52"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>First</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  class\n",
       "0       3  Third\n",
       "1       1  First\n",
       "2       3  Third\n",
       "3       1  First\n",
       "4       3  Third"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_class = titanic[['pclass', 'class']]\n",
    "p_class.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iyw_i9iNg8FX"
   },
   "source": [
    "It seems that both of these columns are the same except that one is numeric and other is text. They basically contain the same information of the class that the passengers paid for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KR-oe15nhaSk"
   },
   "source": [
    "3. How many genders are there in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Yu7X_zNiA7o",
    "outputId": "74abc601-3209-47ab-864f-de67f4f636b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "UEQ0snpFiPty",
    "outputId": "b9e4de38-c9bc-426b-c847-9251eb39fad2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic, x='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9uG41FsmmWMV"
   },
   "source": [
    "4. Show the passengers who were less than 20 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "yDaVIuU_mvzN",
    "outputId": "065ae89f-ee70-4376-ae31-1fd23d01013a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>G</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "7          0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "9          1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "10         1       3  female   4.0      1      1  16.7000        S   Third   \n",
       "14         0       3  female  14.0      0      0   7.8542        S   Third   \n",
       "16         0       3    male   2.0      4      1  29.1250        Q   Third   \n",
       "\n",
       "      who  adult_male deck  embark_town alive  alone  \n",
       "7   child       False  NaN  Southampton    no  False  \n",
       "9   child       False  NaN    Cherbourg   yes  False  \n",
       "10  child       False    G  Southampton   yes  False  \n",
       "14  child       False  NaN  Southampton    no   True  \n",
       "16  child       False  NaN   Queenstown    no  False  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_20 = titanic[titanic['age'] < 20 ]\n",
    "\n",
    "less_than_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GxC6CGshm-2z",
    "outputId": "424f3348-ef82-494b-c3fa-6d23311b0161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(less_than_20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OVNLLpsPnypd"
   },
   "source": [
    "5. Show the categories of the passengers in the column `who`. Use Pie chart to illustrate their values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "Ac3kKqL9n6Xl",
    "outputId": "273c8dd5-8f82-495a-8d32-5a24b9a33410"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='who'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.who.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eX9pazZsqfoQ"
   },
   "source": [
    "6. How many unique cities are there in the column `embark_town`? Plot their occurences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mm982ssPqobJ",
    "outputId": "6fbb2c0f-d6ed-428b-c64a-e772129cce9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['embark_town'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "rMMMGA5-qxQK",
    "outputId": "79d2f8a0-5360-4f74-cfdd-3dee8d511c1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['embark_town'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cgVgUCWhzGaH"
   },
   "source": [
    "There is no limit to how deep youu can go to understand the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C1-JM_QozCYp"
   },
   "source": [
    "<a name='6'></a>\n",
    "## 6. Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZQQaz78GzD2n",
    "outputId": "8118ba0f-65f4-41da-bdd2-0300d9124a06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vXV0gwS1zWqJ"
   },
   "source": [
    "There are missing data in age and deck columns. We can also use heatmaps to show the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "ZOrhkTtLzbAj",
    "outputId": "3310f510-4567-426c-8e36-d7ad7a2104f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rMezfod50HW4"
   },
   "source": [
    "<a name='7'></a>\n",
    "## 7. More Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "Hj4yCpBc0JRW",
    "outputId": "fe682c5d-f35f-40c9-e8fe-2052cce8536c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic, x='survived', palette='autumn', hue='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "4b4kkoKO0aAX",
    "outputId": "42aa1864-3630-4b4a-cc0e-619c516b036c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic, x='survived', palette='viridis', hue='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "v0sR1UGF0x_T",
    "outputId": "0fb2397e-276b-4322-f516-f48c46c78689"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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APg6cOoY6JKlJ4+jqOQT49qz1rcDzdnxSkrXA2m71B0lunud+Dga+u6AK+zeptVnX/ExqXTC5tY2krrxj3idFLkpdC9jv7l7vsdb1c3M1TuyZu1W1Dli30O2TbKyq6UUsadFMam3WNT+TWhdMbm3WNT991TWOrp47gKfPWj+0a5MkjcA4gv/LwOFJDkuyD/Aq4Iox1CFJTRp5V09VPZjkD4HPMhjO+YGq+noPu1pwN9EITGpt1jU/k1oXTG5t1jU/vdSVKi9YIEkt8cxdSWqMwS9Jjdkjgz/JyUluTnJLkrPGWMcHkmxLcsOstmVJrkqypbs/aAx1PT3JF5LcmOTrSd44QbXtm+SaJF/paju3az8sydXde3pRNzBg5JLsleS6JFdOSl1JbkvytSTXJ9nYtU3Ce3lgkkuT3JRkc5LnT0hdR3Q/q5nbfUnOnJDa/qT73N+Q5MLu/8Oif8b2uOCfsCkhPgScvEPbWcCGqjoc2NCtj9qDwJur6ijgeOAN3c9oEmr7MXBCVR0NrAZOTnI88G7gvKp6JnAPcMYYagN4I7B51vqk1PWSqlo9a8z3JLyX5wOfqaojgaMZ/NzGXldV3dz9rFYDzwF+CFw27tqSHAL8MTBdVb/IYPDLq+jjM1ZVe9QNeD7w2VnrZwNnj7GeVcANs9ZvBlZ0yyuAmyfgZ3Y5cNKk1QbsD1zL4Mzu7wJ7z/Uej7CeQxkEwgnAlUAmpK7bgIN3aBvrewk8Bfgm3QCSSalrjjp/BfiPSaiNh2c1WMZgxOWVwMv6+IztcUf8zD0lxCFjqmUuy6vqzm75LmD5OItJsgo4BriaCamt6065HtgGXAV8A7i3qh7snjKu9/S9wFuAn3brT52Qugr4XJJN3VQnMP738jBgO/DBrmvs/UkOmIC6dvQq4MJueay1VdUdwF8C3wLuBP4X2EQPn7E9MfiXjBr8Ch/beNokTwQ+AZxZVffNfmyctVXVQzX4Gn4og0n9jhxHHbMl+TVgW1VtGnctc3hhVR3LoHvzDUleNPvBMb2XewPHAu+rqmOA+9mh62QCPv/7AKcAl+z42Dhq6/6mcCqDX5o/CxzAzl3Fi2JPDP5JnxLi7iQrALr7beMoIsnjGYT+R6vqk5NU24yquhf4AoOvtwcmmTnhcBzv6QuAU5LcxmBG2RMY9GGPu66ZI0WqahuDvurjGP97uRXYWlVXd+uXMvhFMO66Zns5cG1V3d2tj7u2lwLfrKrtVfUT4JMMPneL/hnbE4N/0qeEuAJY0y2vYdC/PlJJAlwAbK6q90xYbVNJDuyW92Pwt4fNDH4BvHJctVXV2VV1aFWtYvCZ+teqeu2460pyQJInzSwz6LO+gTG/l1V1F/DtJEd0TScCN467rh28moe7eWD8tX0LOD7J/t3/0Zmf2eJ/xsb5h5Ue/0jyCuC/GfQN/9kY67iQQV/dTxgcAZ3BoF94A7AF+DywbAx1vZDB19ivAtd3t1dMSG3PBq7rarsB+POu/RnANcAtDL6aP2GM7+uLgSsnoa5u/1/pbl+f+bxPyHu5GtjYvZefAg6ahLq62g4Avgc8ZVbb2GsDzgVu6j77HwGe0MdnzCkbJKkxe2JXjyTpURj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTH/B0yJvO9tnhEcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.age.plot(kind='hist',bins=30,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "9QmPDC3W1Rcm",
    "outputId": "30c91331-94d8-4086-bb4e-34f0f858b6f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='fare'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=titanic, x='age', y='fare', hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PhlE10rl1xBp"
   },
   "source": [
    "That is obvious. The passengers who were in the first class paid more than other classes. And many females were in the first class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "MUzqN-CZ1tTE",
    "outputId": "7606b20f-6ea2-4151-bc9e-1006dfb65636"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='fare'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=titanic, x='age', y='fare', hue='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCppb5Wp3GUr"
   },
   "source": [
    "The purpose of this lab was to learn about the exploratory analysis. There is no limit to what you can do. The more time you spend with the data, the good your analysis will be. I used questions to make it simple and lead the way but there is no proper format for this. The goal is to be one with data (CC: Andrej). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='8'></a>\n",
    "\n",
    "## 8. Checking Correlating Features\n",
    "\n",
    "Checking correlation can help you to see the similarity between features. If two features correlate, that means they contain the same information and if one of them is removed, the analysis/model can be less affected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.557080</td>\n",
       "      <td>-0.203367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.094035</td>\n",
       "      <td>0.135207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.280328</td>\n",
       "      <td>0.198270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>-0.253586</td>\n",
       "      <td>-0.584471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>-0.349943</td>\n",
       "      <td>-0.583398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.182024</td>\n",
       "      <td>-0.271832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adult_male</th>\n",
       "      <td>-0.557080</td>\n",
       "      <td>0.094035</td>\n",
       "      <td>0.280328</td>\n",
       "      <td>-0.253586</td>\n",
       "      <td>-0.349943</td>\n",
       "      <td>-0.182024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <td>-0.203367</td>\n",
       "      <td>0.135207</td>\n",
       "      <td>0.198270</td>\n",
       "      <td>-0.584471</td>\n",
       "      <td>-0.583398</td>\n",
       "      <td>-0.271832</td>\n",
       "      <td>0.404744</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            survived    pclass       age     sibsp     parch      fare  \\\n",
       "survived    1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307   \n",
       "pclass     -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500   \n",
       "age        -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067   \n",
       "sibsp      -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651   \n",
       "parch       0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225   \n",
       "fare        0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000   \n",
       "adult_male -0.557080  0.094035  0.280328 -0.253586 -0.349943 -0.182024   \n",
       "alone      -0.203367  0.135207  0.198270 -0.584471 -0.583398 -0.271832   \n",
       "\n",
       "            adult_male     alone  \n",
       "survived     -0.557080 -0.203367  \n",
       "pclass        0.094035  0.135207  \n",
       "age           0.280328  0.198270  \n",
       "sibsp        -0.253586 -0.584471  \n",
       "parch        -0.349943 -0.583398  \n",
       "fare         -0.182024 -0.271832  \n",
       "adult_male    1.000000  0.404744  \n",
       "alone         0.404744  1.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = titanic.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      1.000000\n",
       "pclass       -0.338481\n",
       "age          -0.077221\n",
       "sibsp        -0.035322\n",
       "parch         0.081629\n",
       "fare          0.257307\n",
       "adult_male   -0.557080\n",
       "alone        -0.203367\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation['survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to check how the features correlate with the label (`survived`, just for example), here is how to do it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y0b9zOD8cO4aHiwvxoSF8ud+axT04YSKnCgr4Yl8ry+rqJJzOIqJ9AGEBfmSXmJnaszuPfLWiUZvuIR3527SJ/P6jrygoK2947ZksfD3caeflSWFZOZdFhZOQmW3rLlyyQZN7M2hybwBO7ktl95pD9BgWy5mT2bh7ujUpT7DUWqgoq8TL15PamloS96XRpVdnAOIGdeHI9kT6jomnzFROQWYxAUH+Nu9Tc1mffJD1ydZzSe+QKMbH9GVnxgmiA0Mor668YHnC1T2G4enqzvt71to6XAfgwFOvzaQlanA7AQVa6yVKqSLgdiAVazK6kobZ1Iv5FPgz4K+1blInoLU2K6V2YS09+E5rXQuUKKVSlFLXaa0/V9Zp3D5a6wPAVqwzvUuAG5qlky3shWfnM3hgDAEB3qxd+TdefWM1y5Y79m2NLsXxvFS6dYziT6Nuprq2mi8PN5xs7hk2l/9s/xg3ows3DrgKo8GIAUVyQQY70w/ZMermsfd0KgPCovjPzJuprKnhtW0/1K97cdo8HvxuKQBv/bSeu4dPws3FhX2n09h3OrW+3YioOLamNp2l7REcRn6piRxz6/jZbU9GKoPCo3jjWutYvLK5YSxenjGP+5Zbx+LNbev5w2jrLdP2ZqSxJyMVgJsGjSDMPwCtIcdcUn8HheFRXbm8ex9qtYWqmhr+sWFlk207EovWvLJhPS/MmInRoFiZkEBqQQELhgzlRE4O21KSWZGQwKOTp/DhTQswVVTw1Cprovf1wQM8NHES79xwIyhYfeQIyfl59ArtxOT4HiTl5bJ4rvU0+Pa2rfyUlmrHnv5varXmuRXreX3+LAxK8fX+wyTl5vP7scNJOJPFxhPJ3DdpNF5urrx43TQAsopN3PvJcixa888fNrL4pmtRKI5kZvPlntZ9/ojpF8nJ/Wm8fv8SXN1cmHbnhPp1bz3yCbc/N4ea6lo+ef5bamstaIuFqF7h9BvfA4DoPhGkHErnzQeXYjAoxs8bjpfv+fNPrdOhrFR6h0Tx7JSbqaqt4d3dDeeSxybMY9GPS2nn6cO0+MvILCngbxOsvwCuTzrA5tQEe4VtW20/v0U195XGSqkpwIuABagGfgd4Am8DJcAGrBecnb3IzKy1/sc5rw8GTgNPaa2frFu2oO4199Q9vxb4HBirtd5Yt6wL8DoQCrgCn2itF13gIrM//prbhEmJQoOrnmsbFx5cqhPZskucVd26L0BvViaz7Bdn5Rda7B2Cw7j/ylZxF06b2Jwmx8hZb8261yFSy55jHmm2/ykJG59ziD6dryVKFFYDqy+wKu4CbZ+4wLLs8+PSWr8HvHfO8y847/uH1joF6wVu579fCjDsnEV//ZnwhRBCCCHaNodMSZuXfMUUQgghhHAiWm4TJoQQQgghROsiCa4QQgghhGhTpERBCCGEEMKZSImCEEIIIYQQrYvM4AohhBBCOJO2P4ErCa4QQgghhDNxhjsTS4mCEEIIIYRoU2QGVwghhBDCmTjBRWaS4AohhBBCOJO2n99KiYIQQgghhGhbZAZXCCGEEMKZSImCEEIIIYRoS5zhLgqS4AohhBBCOJO2P4ErNbhCCCGEEKJtkQRXCCGEEMKZKNV8j1/clJqqlDqulDqplHr4AusjlFLrlVL7lFIHlVJXNEcXJcEVQgghhBDNTillBF4FLgd6AHOVUj3Oa/ZX4DOtdX9gDvBac2xbElwhhBBCCNESLgNOaq2TtdZVwCfAjPPaaMCv7m9/4ExzbFguMruIq56LtHcIDuPbR9LsHYJDmPV32SfO2nTEYu8QHIabm70jcBz3XSEfKWe9t7XW3iE4jOoae0cgzqeb8TZhSqmFwMJzFi3WWi+u+zsMSD9nXQYw5Ly3eAJYo5T6P8AbmNgcccnZSAghhBDCmTTjXRTqktnFv9jw4uYC72mtX1JKDQM+VEr10lpf0kyKlCgIIYQQQoiWcBoIP+d557pl57oN+AxAa70d8AA6XOqGJcEVQgghhBAtYRcQq5TqopRyw3oR2TfntTkFTABQSsVjTXBzL3XDUqIghBBCCOFEmrMG92e3o3WNUuoeYDVgBN7RWicopRYBu7XW3wB/Av6rlLoP6wVnC7TWl/yPrUmCK4QQQgghWoTWegWw4rxlj53z9xFgRHNvVxJcIYQQQghn4gT/VK8kuEIIIYQQzsQJEly5yEwIIYQQQrQpMoMrhBBCCOFU2v4UriS4QgghhBBORLf9/FZKFIQQQgghRNsiM7hCCCGEEM7ECWZwJcEVQgghhHAqbT/DlRIFIYQQQgjRpsgMrhBCCCGEE3GGi8wkwRVCCCGEcCZOkOBKiYIQQgghhGhTZAZXCCGEEMKptP0pXElwhRBCCCGciNTgihY1rftounWMoqq2hi8P/cAZU26TNgsGzsDX3QuDMpBaeIZvjmxAo+vXj4zszxXdR/H0usWUVVfYMnybWPT4bEaPiqegwMw11//D3uG0OK01ez/fzJmENIxurgy9cQKBER2btFv/n2+pKCnFUmshqGsnBs4ejcFgYOvbqynJLgSgurwKV083Ln90jq278ZsMjozkntFjMCgDKxIO8/Ge3Y3WuxqNPDxpCnFBQZRUVLBo5QqyTSUYDQYemDCR2I5BGA0G1hw7yse7d+FqNPKvWdfhajRiNBjYeDKR93/aYafe/W8GhkfyuxHWsVh19DCf7T9vLAxGHhg/hdiO1rF4bq11LHzdPfjr5CuJCwrmh+NHeG3LhvrXjO3ajdn9BwOa/NJS/r5uFSUVreucobXmhw83k7Q/DVd3V6YtnEBIl6bHx0dPL8NcVIaLmxGAOQ9Nx9vfi4ObjrLu4234tvMGYOCkPvQb18OmfbgUgyMjuWfMWIzKwPcJh/l4965G612NRh6ZPIW4oGBKKsp5coV1v3AxGLh/wkS6BQWjteaVjRs4cDqj0Wufvmo6nfz8ufWjD23ZpWYxJCqSe8eNxaAMfHf4MEt2Nh6XvmFh/GHcGGI6duSJ71awITGx0XovNzeWLLiJzSeTeHndehtGLlpSq0pwlVJjgQe01tPsHMoli+sQSXuvAF7a/AHh/iHM6DGO13/6rEm7j/evpLK2CoB5/a6gd0hXDmZZD05/Dx+6doigsLzEprHb0vJvd/Hxp1t4ZtFce4diE5kJaZhyi5n2xHzyU7PZ/ckGJv/5uibtRt42BVdPN7TWbHlrFel7k4gcFMuI26bUt9n75RbcPN1tGf5vZlCKe8eO48FlX5FrNvP67LlsS0kmraCgvs3lPXpiqqzgxg/eY1xsHAtHjOSpVSsY0zUWV6OR25cuwd3FhXfn38S648fJNpVw/7Ivqaiuxmgw8O9rr2dnWipHs7Ls2NNfZlCKu0eO49HvviKv1My/r5nLjrRkThU2jMWU+J6YKyu49eP3GBMTx61DRvLc2hVU1dbwwa7tRAa2JyqwfaP3vGvEGBZ++gElFRXcNnQk03v1Y8nu1pHwn5V0II3CrGLuemk+Z5KyWfXeBhY82fT4AJj++0mERgc1WR4/NJYpN49u6VCbnfUYGV93jJh4Y848tiUnNTpGrujZE1NlJfPff5dxcXHcOXIki1auYFqv3gDc9tGHBHh68sKMmdz1ydL6qZJRMV2pqK62Q68unUEp7p8wnvu++Iock4m3bpjHlpNJpJ4zLtkmE8+uWsPcQQMv+B53jBjOgYzTtgrZMTjBDK5cZGYnPYKi2XfmGADpxVl4uLrj6+bVpN3Z5NagDBiV8Zy5W7iy22hWndhqi3DtZs/eZIqLy+wdhs1kHEwhakg3lFJ06BJCVXkV5cWlTdq5eroBoC0WLDWWJicrrXV90tsadA8O4XRRMZklJdRYLKxLPMHw6JhGbUZEx7Dm6FEANp5MZEB4eP06T1dXDErh7uJCdW0tZVWVAPUf2i4GAy4GA1rj8LoFhZBZUkyWyToWG5NOMCyq8VgMi4ph7QnrWGxOTqRfmHUsKmtqSMg6Q3VtbaP2Sll3EA8XVwC8XN3ILzW3dFeaXeKeFHqNtB4fYV1DqCytwlzY9Phoi7oHh3CmuIjMkmLrMXLiOCMucIysPnIEgI2JiQwIjwAgMjCQfenpABSVl2OuqqRbcDAAHq6uXDdgAB/u/MmGvWk+8SEhZBQVcabYOi5rjx9nZNfG45JVUkJSXh6WC5wAugUF0c7Li51pabYK2UGoZnw4JrvP4CqlooBVwB5gAJAA3AT0BP4FeAOVwITzXndZ3XoPoBy4RWt9XCnVE3gXcMOawM8CzgCfAZ0BI/CU1vrTlu7bz/Fz96G4wlT/vKTCjJ+HD6aqpsncgoEzCPcP5kReGoezTgIQ3zGakkozWaY8m8UsWl55cSneAT71z70CvCkrKsXT37tJ2/X/+Yb81Bw69YwgvH/jE3ruyUw8/DzxDQpo6ZCbRQcfb3LMDcdDntlEfHDIRdtYtKa0qhI/Dw82nkxkeHQ0X9x+B+4urry2aSOmSmuCa1CKN+bMI8zfn68PHuRYtmPP3gK09/Ym97yx6HbeWJzb5tyxuFjJQa3Fwn82r+P16+dTWV3D6eJCXt3S+n6KNRWW4te+4fjwDfTGVFiKT7umx8f3i39EGRTdBscw4upB9Un+8Z1JpB87Q2BIABPnj8Cvva/N4r8UHXx8yDE17Be5ZjPxIecdI94+jY4Rc6V1v0jKy2N4dDQ/Hj9GkK8vcUFBBPn6ciw7m1uHDeezvXuoqK6xaX+aS8fzx8VkpkdoyM+8ooEC7hk7mkUrVjEoMqKFIhT2YvcEt0434Dat9Val1DvAPcBdwGyt9S6llB/WJPZcx4BRWusapdRE4FmsyexdwL+01h8ppdywJrRXAGe01lcCKKX8LxSEUmohsBBg6h9m0/+K4c3e0d/ivT3LcTEYub7PFGLadyatMJOx0YN4Z8/X9g5N2NG4e6ZTW13Dtvd+IPv4aULjG2Y003afIGJg65i9vVTdg4OxWDTXvf0Wvu7u/Ova69ibforMkhIsWrPw44/wdnNn0bRpRAW2J7Ug394h25zRYODKHn2454ulZJYU8/uRY5ndfzAf791p79BaxPTfT8I30IfK8iq++tcqDm85Tu9R3enavws9hsXh4mpk34+H+e7NH5n36NX2DrfFrUg4TERgIG/OnUd2iYnDmZnUWjQxHTrSyd+f1zZtJNjXz95h2tzMfn3ZnpJKrrn1/ZpxyRx34rXZOEqCm661Pvtb+xLgL0Cm1noXgNa6BBp+ZqvjD7yvlIoFNOBat3w78BelVGfgK611olLqEPCSUuoF4Dut9eYLBaG1XgwsBnh09b+b/cfMoeF9GNS5JwCnS7Lx9/AFMgHw8/ChpOLiB1mNpZajOcnEB0Vjqiyjnacffxg+z/padx/uGTaX13Z8ivkCM8DCsZ3YeIikrQkAtI8MprTIzNnLZsqKSvEKaDo7dZbR1YXOfbpw+mBKfYJrqbWQfiCZqQ9d39KhN5s8cylBPg0zaR18fMktLb1gmzyzGYNSeLu5U1JRwYRu3dl1KpVai4Wi8nIOn8kkLjiYzJKG2vTSqkr2Z2RwWWSkwye4+aWldDxvLPLPG4uzbfJKG4/FxcS0t+5RmSXFAGxKSuT6/oNaIPrmt+eHQ+xfbz0+QqODKclvOE+aCkrrLxg7l2+gdZbX3dONnsNjOZOcQ+9R3fHy9ahv03dcD9Z/sr2Fo28+eWYzQb4N+0VHHx/yzkvM8krNjY4RH/eG/eK1TRvr271y3WwyigrpG9aZbkHBfHzLrRiVgQAvL16edS33ffmFbTrVDHLPHxdfn1+dsPbqFErfsDBm9u2Dp5sbrgYD5dXVvLF5S0uF6zDkLgq2c34yWYK19ODnPAWs11rPrCtz2ACgtV6qlPoJuBJYoZS6U2u9Tik1AOtM7tNKqR+11ouatQe/wo70g+xIPwhAtw5RDI3ow8GsE4T7h1BRU9mkPMHN6Iq70RVTVRkGpejWIYrUojNkm/N5dsNb9e0eHL2AV7d/0ibvouAM4sb0Jm6M9SKQ04dTSdx4iMiBseSnZuPq6dakPKG6ooqaymo8/b2x1Fo4cziNjjGh9euzjqXjF9wOr3Y+tBbHsrMICwggxM+PPLOZ8bFxPLN6ZaM221KSmBwfz5GsTMZ0jWVfhrWmMMdkon/ncH44dgwPFxfiQ0P4cv8+/D09qam1UFpViZvRyMDwCD45784Mjuh4Thad/AMI9vUjv9TMmJg4Xvix8VjsSE1iYlw8R7MzGRUdy4Ez6T/7nnmlZiLbtcffw5PiinIGdI4g/ZyL1hzZwEm9GTjJenyc3JfKnh8O0WNYLGeSsnH3cmtSnmCptVBRVomXrye1NbWc3JdGVK/OAJjPKWdI3JNK+07tbNuZS2A9Rto1HCNx3Xh61XnHSHIyU3r0sB4jsbH1dbfuLi4ooKKmhoEREdRqC2kFBaQVFPDNIetnUrCvH89Nn9GqkluAY1lZhAe0I9TPj1yzmYnduvHkipW//EJg0YpV9X9f3rMH3YODnSK5dRaOkuBGKKWGaa23A/OAHcCdSqnBdSUKvjQtUfAHzl72uODsQqVUNJCstf63UioC6KOUOgYUaK2XKKWKgNtbuD+/6HheKt06RvGnUTdTXVvNl4fX1q+7Z9hc/rP9Y9yMLtw44CqMBiMGFMkFGexMP2THqG3vhWfnM3hgDAEB3qxd+TdefWM1y5a3zZ9VATr1jCQzIY3vnliC0c2FIfMbSs9XPvsJlz86h5qqGja9sYLamlrQmqC4MLqO6lXf7tSek63m4rKzLFrzyob1vDBjJkaDYmVCAqkFBSwYMpQTOTlsS0lmRUICj06ewoc3LcBUUcFTq1YA8PXBAzw0cRLv3HAjKFh95AjJ+XlEt+/AQ5MnY1AKg1JsSExkR2qKnXv6yyxa89qW9Txz5UwMSrHmeAJphQXcOGgoibk57EhLZtWxBP48fgrvzF2AqbKC535YUf/692+4FS9XN1yMBoZFxfCX75dxqrCAJXt28OKM66i11JJtMvHS+jV27OVvE9MvkqQDabzxpyW4urlw5cKG4+PtRz/htmfnUFNdy6cvfEttrQVtsRDVM7z+VmC71xwkcW8KBqMBD28Ppt054WKbcjgWrfn3hnX8/eprMCjFyiMJpBbkc8vQYRzPzmZbSjLfJxzm0SlTWXLzLZRUVPDUSut+EeDpxd9nzkRrTZ65lOdWr/qFrbUetVrzz3Xr+OesazAYFN8fTiAlP5/bhg/jWHY2W5OS6R4czLMzrsLXw4MRMdHcNnwYN77/gb1Dty8nmMFV2s6XFZ9zkdluYCBwBLgR60VmrwCeWJPbicAg6m4TppQaBrwPlALfA/O11lFKqYfrXl8NZGFNmAcDLwKWuuW/01r/7FROS5QotFbfPuJsV5de2Ky/R9o7BIex6YjF3iE4DDc3e0fgOOYMlBvznPX+ttpfbuQkWun1ay1iy5/uc4jUMvr6Z5otx0n+7C8O0afzOcoMbo3Wev55y3YBQ89btoGGUoTtQNw56/5at/x54PnzXre67iGEEEIIIdo4R0lwhRBCCCGEDchFZjagtU4Fev1SOyGEEEII0QycIMGVgikhhBBCCNGmSIIrhBBCCCHaFLuXKAghhBBCCBtSbb9GQWZwhRBCCCFEmyIzuEIIIYQQTsQZ7qIgM7hCCCGEEKJNkQRXCCGEEEK0KVKiIIQQQgjhTJygREESXCGEEEIIZyJ3URBCCCGEEKJ1kRlcIYQQQggnou0dgA1IgiuEEEII4UzafoWClCgIIYQQQoi2RRJcIYQQQghnoprx8UubUmqqUuq4UuqkUurhi7S5Xil1RCmVoJRaemmds5ISBSGEEEII0eyUUkbgVWASkAHsUkp9o7U+ck6bWOARYITWulApFdQc25YZXCGEEEII0RIuA05qrZO11lXAJ8CM89rcAbyqtS4E0FrnNMeGZQb3Ik5kO8M1hr/OrL9H2jsEh/Dln9PsHYLDmPpMhL1DcBgFpfaOwHG8v63W3iE4jLwCi71DcBieHk5wRVNr04z3wVVKLQQWnrNosdZ6cd3fYUD6OesygCHnvUVc3ftsBYzAE1rrVZcalyS4QgghhBBORDfjd466ZHbxLza8OBcgFhgLdAY2KaV6a62LLiUuKVEQQgghhBAt4TQQfs7zznXLzpUBfKO1rtZapwAnsCa8l0QSXCGEEEII0RJ2AbFKqS5KKTdgDvDNeW2+xjp7i1KqA9aSheRL3bAkuEIIIYQQzsRGtwnTWtcA9wCrgaPAZ1rrBKXUIqXU9Lpmq4F8pdQRYD3woNY6/1K7KDW4QgghhBDOxIbX/WmtVwArzlv22Dl/a+D+ukezkRlcIYQQQgjRpsgMrhBCCCGEU2n7t26TBFcIIYQQwpm0/fxWShSEEEIIIUTbIjO4QgghhBDOxAlmcCXBFUIIIYRwItreAdiAlCgIIYQQQog2RWZwhRBCCCGciROUKMgMrhBCCCGEaFMkwRVCCCGEEG2KlCgIIYQQQjgT1fZrFCTBFUIIIYRwJm0/v5USBSGEEEII0bbIDK4N3Tp4DP3DoqiqreE/W9eQUpDbpE10YBB3j5iEm9GFfadTeWfXRgDuG305nfzaAeDt5k5pVSUPfreUUV26Mb3nwPrXR7brwJ+/W0pqYZ5tOtUMtNbs/XwzZxLSMLq5MvTGCQRGdGzSbv1/vqWipBRLrYWgrp0YOHs0BoOBrW+vpiS7EIDq8ipcPd24/NE5tu5Gi1v0+GxGj4qnoMDMNdf/w97h2MSM+DF07xhFdW0Nnx5aw+mSpsfM7YNm4OvujUEZSCk8w7KE9Wg0fUK6MqnrUIJ8Anll2ydklOTYoQfNY27fMfQOjaKqpoZ3dq/hVFHTcZjZcxjDI+PxcnPn7q9fr1/e3suXWwZNwsfNk9LqCt7auZrCcrMtw78kgyMjuWfMWIzKwPcJh/l4965G612NRh6ZPIW4oGBKKsp5csUKsk0luBgM3D9hIt2CgtFa88rGDRw4nQGAi8HAvWPH07dzZ7TWvL19K5tOnrRH936zEV2jeOjycRiV4qu9h3l7y85G628aNpBrBvSm1mKhoKyMx75eTWaxCYD7Jo1mdFwXDEqxPSmN51eut0cXWsTQ6Cj+NHEsBoOB5fsP8cGOxvvLvMEDmN7POi5FZeU89f1qskpMdopWtCRJcG2kf1gUoX4B/N/X7xPbIYSFQ8bzyMpPm7S7Y+g43tj+I4l5Wfxlwgz6d4pk35k0Xt60sr7NTQNHUVZdCcDmlONsTjkOQERAe/48blqrSm4BMhPSMOUWM+2J+eSnZrP7kw1M/vN1TdqNvG0Krp5uaK3Z8tYq0vcmETkolhG3Talvs/fLLbh5utsyfJtZ/u0uPv50C88smmvvUGyie8coOngH8MKm94kICOGanuN5ZXvTY+bD/SuprKkC4Kb+V9InNJYDmSfIMuXzwb7vmNVzgq1Db1a9Q6II9g3g0VXvEx0Ywo0DxvPMuqbjcCAzhXVJB3h26s2Nll/fZxTb0o6yLe0o3Tt2Zlav4by1a42twr8kBqW4d+x4Hlz2FblmE2/Mmce25CTSCgrq21zRsyemykrmv/8u4+LiuHPkSBatXMG0Xr0BuO2jDwnw9OSFGTO565OlaGD+ZUMoLC/jpg/eQwG+Hh726eBvZFCKv1w5gYUffEFWiYlPFt7A+uMnSc5tGJejmTnMWbyEiuoarh/cl/snj+HBz7+jb3gn+kd0YtZrHwDwwW1zGBTVmd2pGfbqTrMxKMWfJ4/nnk++JKfExPsLbmBzYhIp+Q3jcjw7l5vf/YjKmhpm9e/D/40bzV+Wf2/HqO1EShREcxkcHs2GpKMAJOZl4eXmToCnV6M2AZ5eeLm6kZiXBcCGpKMMjohp8l7Do2LZknKiyfKRXbqx9QLLHV3GwRSihnRDKUWHLiFUlVdRXlzapJ2rpxsA2mLBUmNpcoBqreuT3rZoz95kiovL7B2GzfQMimbPaesxc6ooCw8Xd3zdvZq0O5vcGpQBo8EA2vpv9OSUFpJbWmSzeFtKv07RbEuzjkNyQRZeru74ezQdh+SCLIormu4fob6BHM1JB+BYbgb9OkW3bMDNqHtwCGeKi8gsKabGYmHdieOMiG58ThwRHcPqI0cA2JiYyIDwCAAiAwPZl27td1F5OeaqSroFBwNweY+eLN1lnfHUQElFhY161Dx6h4VwqqCIjMJiamotrDx8nHHduzZqsys1nYrqGgAOpmcS7OdjXaE17i4uuBqNuLkYcTEYyDe3jfNKz04hZBQWcabIur+sOXqM0XGN95c9p9KprLGOy6EzmQSdHRfR5rTaBFcp9bVSao9SKkEptbBu2W1KqRNKqZ1Kqf8qpf5Tt7yjUupLpdSuuscIW8fb3suH/LKGnwULysy09/L5n9vEB3WiuLyMLFNRk20Mj4plS2rrS3DLi0vxDmjop1eAN2VFTRNcgPX/+YavHnoXVw9Xwvs3PnHlnszEw88T36CAlgxX2Iifhw9FFQ3HQ3GFGX/3C38Y3T7oah6fcAeVNdUczGpdPzX/knaePhScc14oLDcT4PnrP5TTi/MYGGZNfgZ0isHT1R1vt9YxY9nBx4ccU8PPx7lmMx18Gve9g7cPOWZrG4vWmCsr8fPwICkvj+HR0RiUIsTPj7igIIJ8ffF2s/7Cc+uw4bw5dx6PX3El7byafmFwZEF+PmQVN4xLdrGJYN+L7xPXDOjFlsQUAA5kZLIzJZ11D9zJugfuYmtSKil5BRd9bWvS0ceH7HPKDXJMZjr6+l60/fS+vdmelGqDyByQasaHg2q1CS5wq9Z6IDAI+INSKgz4GzAUGAF0P6ftv4CXtdaDgVnAWxd6Q6XUQqXUbqXU7uT121o2+t9oZJdubEk93mR5bIdgKmtqSC/Kt0NUtjPununMfG4BtTW1ZB8/3Whd2u4TRAxsm7O34ue9tftrnlr3Fi4GI13bh9s7HIfy+cHNxHUM4/EJc+nWMYyCMhMWbbF3WC1uRcJhcs1m3pw7j3tGj+VwZia1Fo3RoAjy9eVwZiZ3fryUI5mZ3DVytL3DbTHT+sTTo1Mw727dDUB4YADRHQOZ+M/FTHjpTYZ0iWBARJido7S9qT3jiQ8J5sOfdts7FNFCWnMN7h+UUjPr/g4HbgQ2aq0LAJRSnwNxdesnAj1Uw33f/JRSPlrrRldaaK0XA4sBrv3gX/pSA5zarQ8TYnsBkJSf3Wg2NvC82VqA/PNmbM9vY1CKIRFd+fP3HzfZ1oio1lWecGLjIZK2JgDQPjKY0iIzZy8rKysqxSvA+6KvNbq60LlPF04fTCE03prMWGotpB9IZupD17d06KIFDY/ow5Bw6zGTXpxNgEfD8eDv4UNx5cUvjqqx1JKQnUTP4GgS80+1eKwtaVxMH0Z3sY5DakE2gV4+UPfdtZ2nD0X/w0ViRRWlvLbdWmPobnRlQFhXyqurmj3mlpBnNhN0zgxcRx8f8syN+55XaibIx5c8sxmDUvi4u9eXHLy2aWN9u1eum01GUSElFRWUV1ez+WQiABsST3BFz1426E3zySkxE+LfMC7B/r5km5ruE0OjI7hj9BBuefdTqmtrAZgQ35WDGZmUV1UDsCUxhb7hndh76nST17c2uWYzwX4N4xLk60OuqekFZIOjIrhl+GXc9dFn9eMi2p5WOYOrlBqLNWkdprXuC+wDjv3MSwzAUK11v7pH2PnJbUtYdfwgD363lAe/W8rOU0mMjYkHILZDCGXVlRSVN657Kiovo6y6itgOIQCMjYlnV3py/fo+oRGcLi5o9HMlWH8hGBYVe8GZXUcVN6Y3lz86h8sfnUNY3y6k/nQcrTV5KVm4errh6d84wa2uaKjLtdRaOHM4Db/ggPr1WcfS8Qtuh1c7qadqzbadOsjLW5fy8talHM5OYmCY9ZiJCAihoqYSU2XjY8bN6Fpfl2tQiu5BXcgxt/6fW9cnHeTJtUt5cu1S9p1JYnikdRyiA63njgvV2l6Mj5tH/a+IV3QfxJbUIy0Qccs4lp1FWEA7Qvz8cDEYGB/XjW3JyY3abEtOZkqPHgCMiY2tr7t1d3HBw8U6hzMwIoJabam/OG17cjL9Olu/HA8IjyC1oHX98nX4TBaRgQGEBfjhYjRwea9ubDiW1KhN95AgHrtqEv+39GsKSsvrl2cWmRgU2RmjQeFiMDAwqjPJua2r/xdz5EwW4e0C6ORv3V8mx3dnc2Lj/SUuuCOPTJ3IA18sp7Cs/CLv5AScoEShtc7g+gOFWusypVR3rGUJ3sAYpVQ7wIS1FOFQXfs1wP8BLwIopfpprffbMuC9p1MZEBbFf2beTGVNDa9t+6F+3YvT5vHgd0sBeOun9dw9fBJuLi7sO53GvtOp9e1GRMWx9QI1tj2Cw8gvNZFjLmnxfrSETj0jyUxI47snlmB0c2HI/Iar3lc++wmXPzqHmqoaNr2xgtqaWtCaoLgwuo5qmHU5tedkm7247KwXnp3P4IExBAR4s3bl33j1jdUsW77zl1/YSh3LTSW+YxQPj7mZqtoaPjvYcMzcN2IeL29dipvRlVsGTMfFYEQpOJmfwY5062HfKziGGT3G4OPmya2DZnCmJJe3dn9tp978dgezUukdEsVzU63j8M7uhnF4fOI8nlxrPXdc23sEQ8K74WZ05cUrbmVzagLfHPmJbh07M6vXCDSaE3mn+WjfBjv15H9n0Zp/b1jH36++BoNSrDySQGpBPrcMHcbx7Gy2pSTzfcJhHp0ylSU330JJRQVPrVwBWC/a/fvMmdYvzuZSnlu9qv59F2/dzCNTpnK3+xiKy8t54YfWcVeJs2otmmdXrOONG2dhNBhYtu8wSbn53D1uOAlnstlwPIk/TR6Nl5srL11/FQCZxSb+8PHX/HDkBEOiw/nq9zejNWw9mcLGE8m/sMXWoVZrXvxhPf+eMwuDUnx78DDJefksHDWco5lZbD6ZzB/GjcbTzZXnZk4DIKvExANfLLdz5LannOBfMlNaX/Iv8TanlHIHvgaigONAAPAE1pKEB4ECrDO6GVrrvyilOgCvAvFYk/pNWuu7fm4bzVGi0Fb06tT2D4Rf48s/p9k7BIcx9ZkIe4fgMAoufD2kU0o+3fZre3+tvAIZi7M8PeQz5Kydj9zvEIMRcfeLzZbjnHr1QYfo0/la5Qyu1roSuPz85Uqp3VrrxUopF2AZ1iQYrXUeMNumQQohhBBCCLtolTW4P+MJpdR+4DCQQl2CK4QQQggh6kgNbuuitX7A3jEIIYQQQgj7alMJrhBCCCGE+AUOPPPaXNpaiYIQQgghhHBykuAKIYQQQog2RUoUhBBCCCGciBPcBldmcIUQQgghRNsiCa4QQgghhGhTpERBCCGEEMKZOEGJgiS4QgghhBDOxAkSXClREEIIIYQQbYrM4AohhBBCOBEnmMCVBFcIIYQQwqk4wX3CpERBCCGEEEK0KTKDK4QQQgjhRJxgAldmcIUQQgghRNsiCa4QQgghhGgRSqmpSqnjSqmTSqmHf6bdLKWUVkoNao7tSoIrhBBCCOFMVDM+fm4zShmBV4HLgR7AXKVUjwu08wXuBX661K6dJTW4F1FdY+8IHMemIxZ7h+AQpj4TYe8QHMaqv5yydwgOwzw21N4hOIyQUKO9Q3AYYwbKx+tZwzvbOwJxPhuW4F4GnNRaJwMopT4BZgBHzmv3FPAC8GBzbVhmcIUQQgghxG+ilFqolNp9zmPhOavDgPRznmfULTv39QOAcK31980Zl3zFFEIIIYRwJs04hau1Xgws/k1hKGUA/gksaL6IrCTBFUIIIYRwIja8TdhpIPyc553rlp3lC/QCNihrUCHAN0qp6Vrr3ZeyYSlREEIIIYQQLWEXEKuU6qKUcgPmAN+cXam1LtZad9BaR2mto4AdwCUntyAJrhBCCCGEaAFa6xrgHmA1cBT4TGudoJRapJSa3pLblhIFIYQQQggnYst/yUxrvQJYcd6yxy7SdmxzbVdmcIUQQgghRJsiCa4QQgghhGhTpERBCCGEEMKJ2LJEwV4kwRVCCCGEcCZOkOBKiYIQQgghhGhTZAZXCCGEEMKJKCeYwpUEVwghhBDCmbT9/FZKFIQQQgghRNsiM7hCCCGEEE7ECSZwJcEVQgghhHAmznCbMClREEIIIYQQbYrM4AohhBBCOBMnmMGVBFcIIYQQwok4QX4rCa6t3TFkDAPDo6isqeFfm9eQnJ/bpE1M+yD+MGoS7i4u7ElP5b8/bQRg3oChDImIwaI1xRVl/HvTDxSUl9IrJIxHJ15FtqkEgB1pJ/l0/06b9ut/NTgykntGj8GgDKxIOMzHe3Y3Wu9qNPLwpCnEBQVRUlHBopUryDaVYDQYeGDCRGI7BmE0GFhz7Cgf796Fq9HIv2Zdh6vRiNFgYOPJRN7/aYedendpZsSPoXvHKKpra/j00BpOlzTdR24fNANfd28MykBK4RmWJaxHo+kT0pVJXYcS5BPIK9s+IaMkxw49aHmLHp/N6FHxFBSYueb6f9g7nBY1unsUj189HoNB8emOQ7yx7sLH9tQ+sby+YAbT//khhzKyCfDy4LUF0+kTHsKXuxJ4/KsfbRx58xsaFckfJ4zFqAx8c/AwH+7c1Wh9v85h/HH8GGI6duSxb1ew/kRi/bpgX18emTqJYF8ftIb7v/yarJISW3ehWV3baww9g6Ooqq3hw31ryChufK5wNbpw26Ar6ODlj9aaQ9kpfHN0KwBDwuO5usdIiitKAdiYcoDtpxJs3ofmoLVm1ftbSNyfhqubC1f/bgKhXTo2affeoq8xF5Xh4mYE4MZHrsLb34viPBNfv/4jFaVVWCwWJs4dRmz/SFt3QzSzVpPgKqXeAv6ptT6ilDJrrX3sHdP/amDnKEL9A7jri/eJ6xjC74aP58FvP23S7q7h43h164+cyM3isckzGNA5kr0ZaSw7tJele61J27QefZndfwivb1sHwJGsMzy99hub9ue3MijFvWPH8eCyr8g1m3l99ly2pSSTVlBQ3+byHj0xVVZw4wfvMS42joUjRvLUqhWM6RqLq9HI7UuX4O7iwrvzb2Ld8eNkm0q4f9mXVFRXYzQY+Pe117MzLZWjWVl27On/rnvHKDp4B/DCpveJCAjhmp7jeWV7033kw/0rqaypAuCm/lfSJzSWA5knyDLl88G+75jVc4KtQ7ep5d/u4uNPt/DMorn2DqVFGZRi0TUTufGNz8kqNrH8vvmsTUjiZHZ+o3be7q7cMmoA+9LO1C+rrKnlnyu3EhfSgW6hHWwderMzKMWfJo3n3s++Isdk4p0b57E5KYnU/IbzRlaJiadWruGGwQObvP6xK6bw3o6d7Eo7haerKxatbRl+s+sRFEVH7wCe/PF9otqFMKfPeP6xuem54seTe0nMz8CoDPzf8GvoERTJkZw0APaeSeTzQxtsG3gLOLn/FAVZxfzfyzdw+mQ237+9kdufvvaCba+5eyKdYoIaLdu0bA89hnZl8KRe5GYU8NEL3/PHV260Rej24wRTuK3mIjOt9e1a6yP2juNSXBYRzfqTRwE4kZuFt5s77Ty9GrVp5+mFl6sbJ3Ktidn6k0cZEhEDQHl1VX07dxdXdCs9QXcPDuF0UTGZJSXUWCysSzzB8OiYRm1GRMew5qh1rDaeTGRAeHj9Ok9XVwxK4e7iQnVtLWVVlQBUVFcD4GIw4GIw0BqHp2dQNHtOW/t9qigLDxd3fN29mrQ7m9walAGjwcDZzuaUFpJbWmSzeO1lz95kiovL7B1Gi+sbEUJaXiHpBcVU11r4dt8xJvWKadLu/stH8sa6XVRW19YvK6+qZnfKaSpramwZcovpERpCRmERZ4qLqbFYWHvsOKO7Nh6LrJISknLzmiSvUe0DMRoM7Eo7BUB5dXWrH5c+IdHszLCeK1ILs/B0dcfvvHNFdW0NifkZANRqC+nFOQR4tLq5oV90bE8KfUZ1QylF59gQKsqqMBWW/vo3UFBZbj2nVpRV4duu6Tm3rVHN+HBUDjmDq5TyBj4DOgNG4Cngd8ADWuvddW1eBiYDWcAcrXWuUuoPwF1ADXBEaz1HKfUEEAN0BToAf9da/9fGXQKgvZcPeaXm+ud5pWbae/lQWF7WqE1+WUOb/Lo2Z80fOIxxMfGUVlfy15Vf1S/vFhTC/7t6HgVlpby7czPpRQ2zGo6mg483OWZT/fM8s4n44JCLtrFoTWlVJX4eHmw8mcjw6Gi+uP0O3F1ceW3TRkyV1gTXoBRvzJlHmL8/Xx88yLHs1jV7C+Dn4UNRRcP//+IKM/7uPpgqmyZztw+6mvCAYI7npnEw66QtwxQ2EuLvS2ZRw7GSVWSmX2RoozY9w4IIDfBl/dFkFo4bbOsQbaajjw85poaxyDGZ6Rka8jOvaBDRrh3mykqemzGNTv7+7Eo7xWubtrTqWdwADx8KyxvOFUXlZgI8fCi5wLkCwNPFjd7B0WxI3l+/rF9oV7q2DyPHXMiXhzc1Ove0JqaCUvzbN3xO+gV6Yyooxbedd5O2y99chzIo4i+LYfTMgSilGDtrMEue+5adqw9RXVnDjY9eZcvwRQtx1BncqcAZrXVfrXUvYNV5672B3VrrnsBG4PG65Q8D/bXWfbAmumf1AcYDw4DHlFKdLrRRpdRCpdRupdTu1I3bmrE7zWfJnu3c9tk7bEw6zpXxfQFIys/ljs/e5Y9fL+X7Iwd4dELbPTi7BwdjsWiue/stbnjvHa4fMIBQPz/Amggv/Pgjrn/nbbqHBBMV2N7O0bast3Z/zVPr3sLFYKRr+/BffoFoc5SCv84YxzPLN9g7FIdmNBjo2zmMVzZs5tYPl9IpwJ8re/Wwd1g2Y1CKBQMvZ0PKfvLLrHXHh7NSeHztuzy34SOO5Z7ixv6T7Rxly7vmnon87u9zuOXxmZw6doaDm48DcHhbIn1Hd+f+V29m3p+vZNlrP6ItrffLz6+hVPM9HJWjJriHgElKqReUUqO01sXnrbcAZ4uNlgAj6/4+CHyklJqPdRb3rOVa63KtdR6wHrjsQhvVWi/WWg/SWg+KGjO8WTpyRXwfXp4xj5dnzKOwvJQO3g3fMjt4N56tBcgvazxj2/4CbQA2Jh1nWFRD6UJFjfXn+T0ZqRgNBnzdPZol/paQZy4lyMe3/nkHH19yS0sv2sagFN5u7pRUVDChW3d2nUql1mKhqLycw2cyiQsObvTa0qpK9mdkcFlk67hIYHhEH+4bMY/7RszDVFna6CdEfw8fiisvPqtSY6klITuJnsHRtghV2FhWsYnQgIZjJSTAh6zihllMH3c34kLa88nds9n81zvoHxnKf2+bSe/OwRd6u1Yt12wmyLdhLIJ8fcg1/7oZxxyTicScXM4UF1OrNZsSk+gWHPTLL3Qwo6P68PCYeTw8Zh7FlaW082w4VwR4+lx0BnZu3wnklhY1mr0tra6gxmItadmWlkBEQOsaj51rDvHGw5/yxsOf4hPgRXF+Q99LCkrxDWw6e+sXaB0vd083eo+I43SS9SLcfeuP0nOY9fM0PC6EmupaykzlNuiFHTlBjYJDJrha6xPAAKyJ7tNKqcd+6SV1/70SeLXutbuUUi7nrT+/fYtbcfQg9y1fyn3Ll7IjLYlxXeMBiOsYQmlVZaPyBIDC8jLKqquI62j96W1c13h2nkoGINQvoL7dkIhoThcVAhBwTh1vbIdgDEphqqxoyW5dkmPZWYQFBBDi54eLwcD42Di2Jyc1arMtJYnJ8daxGtM1ln0Z6YD1g6p/Z+tspYeLC/GhIaQXFOLv6Ym3mzsAbkYjA8MjOFVYaMNe/XbbTh3k5a1LeXnrUg5nJzEwzNrviIAQKmoqm5QnuBld6+tyDUrRPagLOWbHLUkRv93B9CyiOrajc6A/rkYDV/XvztrDDceKqaKKgY+9xqin/8uop//LvrRM7nh7GYcysu0Ydcs4mplFeLt2hPpbzxsTu3dj88nkX/farGx83N0J8PQEYGBEOCn5re+Y2ZR6kOc3LuX5jUs5mJnEZZ2t54qodiGUV1desDxhWvdheLq48+XhjY2Wn1uv2zskmixT6xqPyyb35q7nZ3PX87PpPqgLBzcfR2tNRmIW7l5uTcoTLLUWykqsSWttTS0n9qYS1DkQAP8OvqQcPg1A7ukCaqpq8PLztG2HRLNz1BrcTkCB1nqJUqoIuP28JgbgWuATYB6wRSllAMK11uuVUluAOcDZr7czlFLPYS1tGIu1lMHm9mSkMig8ijeuvZnKmhpe2fxD/bqXZ8zjvuVLAXhz23r+MHoSbkYX9maksScjFYCbBo0gzD8ArSHHXFJ/B4XhUV25vHsfarWFqpoa/rFhpc379r+waM0rG9bzwoyZGA2KlQkJpBYUsGDIUE7k5LAtJZkVCQk8OnkKH960AFNFBU+tWgHA1wcP8NDESbxzw42gYPWRIyTn5xHdvgMPTZ6MQSkMSrEhMZEdqSl27un/7lhuKvEdo3h4zM1U1dbw2cGGfeS+EfN4eetS3Iyu3DJgOi4GI0rByfwMdqQfAqBXcAwzeozBx82TWwfN4ExJLm/t/tpOvWk5Lzw7n8EDYwgI8Gbtyr/x6hurWbbcsW+N91vUWjSPf/UjHyychcFg4POdh0jMzue+qSM4lJ7F2oSkn3395r/egY+HG65GI5N6deWmN79ocgeG1qJWa15au47/d+01GAyK7w4lkJKfzx0jhnE0K5stScnEhwTz/NVX4evuwciYaG4fMYwb3v2g7pyziVdmz0KhOJadzfIDh+zdpUuSkJNKz+AoHp9wM9W1NSzZ13CueHjMPJ7fuJQADx+mxl1GlqmAh8bMAxpuBzY2uh+9g6Op1RbKqitYsv+Hi23K4cX2jyRx/yle+eNHuLq7MOPO8fXr3nj4U+56fjY11bUsef47amtq0RZNl96dGTDBWqYyef5wvv3vBnasOAAKrv7deJQj//beDNp276yUI16Jr5SaAryItRShGusFZv+g7iIzpZQZWIz1IrMcYDZQhLX8wB/r/7slWuvn6y4yiwZi+R8uMpvxzr8cb2DsxGSWoQAYGOsMp4RfZ9VfTtk7BIdhHhv6y42cREio0d4hOIyB3RzyB1K7GN7Z3hE4jnkD7nWID5Lej7/UbB/sh578k0P06XwOOYOrtV4NrD5v8dhz1l/sPicjL7L8oNb6pmYITQghhBBCODj5iimEEEIIIdoUh5zBbU5a6yfsHYMQQgghhKNo4yXGgMzgCiGEEEKINqbNz+AKIYQQQohzOMEMriS4QgghhBBORDlBhislCkIIIYQQok2RGVwhhBBCCCciF5kJIYQQQgjRykiCK4QQQggh2hQpURBCCCGEcCLOUKIgCa4QQgghhBNxgvxWShSEEEIIIUTbIjO4QgghhBDOxAmmcCXBFUIIIYRwIs5QgyslCkIIIYQQok2RGVwhhBBCCCfiBBO4MoMrhBBCCOFUVDM+fmlTSk1VSh1XSp1USj18gfX3K6WOKKUOKqV+VEpFXnoHJcEVQgghhBAtQCllBF4FLgd6AHOVUj3Oa7YPGKS17gN8Afy9ObYtJQoXYTJre4fgMNzc7B2BYygotXcEjsM8NtTeITgMnw2Z9g7BYVTPCrN3CA6jnYcz/Aj8a8nnqaOx4d55GXBSa50MoJT6BJgBHDnbQGu9/pz2O4D5zbFhmcEVQgghhHAiSjXnQy1USu0+57HwnE2FAennPM+oW3YxtwErm6OPMoMrhBBCCCF+E631YmDxpb6PUmo+MAgYc8lBIQmuEEIIIYRzsV2Nwmkg/JznneuWNQ5HqYnAX4AxWuvK5tiwlCgIIYQQQjgRG95EYRcQq5TqopRyA+YA3zSKRan+wJvAdK11ziV3ro4kuEIIIYQQotlprWuAe4DVwFHgM611glJqkVJqel2zFwEf4HOl1H6l1DcXebv/iZQoCCGEEEI4EVv+U71a6xXAivOWPXbO3xNbYruS4AohhBBCOJW2fxs7KVEQQgghhBBtiszgCiGEEEI4EVuWKNiLJLhCCCGEEM7ECRJcKVEQQgghhBBtiszgCiGEEEI4ESeYwJUEVwghhBDCmThDDa6UKAghhBBCiDZFElwhhBBCCNGmSImCEEIIIYQTcYYSBUlwhRBCCCGciBPkt1KiIIQQQggh2haZwbWRwZGR3DN6DAZlYEXCYT7es7vRelejkYcnTSEuKIiSigoWrVxBtqkEo8HAAxMmEtsxCKPBwJpjR/l49y46+vjw8OQptPPyAg3fHT7EVwf226dz/6OB4ZH8boR1LFYdPcxn+88bC4ORB8ZPIbajdSyeW2sdC193D/46+UrigoL54fgRXtuyof41Y7t2Y3b/wYAmv7SUv69bRUlFhW071kzm9h1D79AoqmpqeGf3Gk4V5TZpM7PnMIZHxuPl5s7dX79ev7y9ly+3DJqEj5snpdUVvLVzNYXlZluG3yxGd4/i8avHYzAoPt1xiDfW7bxgu6l9Ynl9wQym//NDDmVkE+DlwWsLptMnPIQvdyXw+Fc/2jhy21v0+GxGj4qnoMDMNdf/w97htKhh0VE8MHksBmXg6/2HeH/7rkbrb7hsADP69abWYqGwrJxF360mq8REXHBHHp46AW93NywWzTtbf+KHoyfs1IvmobVm35ebyUpIw+jmymXzJ9AuvGOTdpte+5by4lK0xUKHmE4MuH40BoOBwyt2krLtCO4+HgD0vmoooT2jbNyL5qG1ZtX7W0jcn4armwtX/24CoV2ajsV7i77GXFSGi5sRgBsfuQpvfy+K80x8/fqPVJRWYbFYmDh3GLH9I23dDdtygincNp/gKqUWAIO01vfYKwaDUtw7dhwPLvuKXLOZ12fPZVtKMmkFBfVtLu/RE1NlBTd+8B7jYuNYOGIkT61awZiusbgajdy+dAnuLi68O/8m1h0/TnVtDW9s3kRibi6erq68MWcee9JPNXpPR2RQirtHjuPR774ir9TMv6+Zy460ZE4VNsQ9Jb4n5soKbv34PcbExHHrkJE8t3YFVbU1fLBrO5GB7YkKbN/oPe8aMYaFn35ASUUFtw0dyfRe/Viye4c9unhJeodEEewbwKOr3ic6MIQbB4znmXWfNml3IDOFdUkHeHbqzY2WX99nFNvSjrIt7SjdO3ZmVq/hvLVrja3CbxYGpVh0zURufONzsopNLL9vPmsTkjiZnd+onbe7K7eMGsC+tDP1yypravnnyq3EhXSgW2gHW4duF8u/3cXHn27hmUVz7R1KizIoxUNTx3P30i/JLjHxwa03sCkxiZS8hnPHsexcvnjnIyprapg1oA9/mDCaR5d9T0V1NY9/s4r0wiI6+Hiz5LYb2J6chrmy0o49ujRZR9Iw5xRz+WPzKUjNZs+nG5j4wHVN2g27ZQqunm5ordn29ioy9iURMTAWgNhxfek+ob+tQ292J/efoiCrmP97+QZOn8zm+7c3cvvT116w7TV3T6RTTFCjZZuW7aHH0K4MntSL3IwCPnrhe/74yo22CN1unKEGt82UKCilHDZZ7x4cwumiYjJLSqixWFiXeILh0TGN2oyIjmHN0aMAbDyZyIDw8Pp1nq6uGJTC3cWF6tpayqoqKSgrIzHXOrNXXl3NqcICOnj72K5Tv1G3oBAyS4rJMlnHYmPSCYZFNR6LYVExrD1hHYvNyYn0C7OORWVNDQlZZ6iurW3UXtUdqR4urgB4ubqRX9r6Zi0B+nWKZluate/JBVl4ubrj7+HVpF1yQRbFFWVNlof6BnI0Jx2AY7kZ9OsU3bIBt4C+ESGk5RWSXlBMda2Fb/cdY1KvmCbt7r98JG+s20VldcP+UF5Vze6U01TW1NgyZLvaszeZ4uKm+0Jb07NTCOkFRZwuKqbGYmHNkWOMiWu8X+xJS6//f3/4dCbBvtZz4qmCItILiwDIM5dSUFpOOy9Pm8bf3E4fSiHqsm4opWjfJYTq8irKi0ubtHP1dANAWyxYai22DtMmju1Joc8o61h0jg2hoqwKU2HTsbgoBZXlVQBUlFXh267pOVe0Pg6VFCqlooBVwB5gAJAA3AQ8AFwFeALbgDu11loptQHYD4wEPlZKbQL+BXgDlcCEurfupJRaBcQAy7TWf7ZRlwDo4ONNjtlU/zzPbCI+OOSibSxaU1pViZ+HBxtPJjI8Opovbr8DdxdXXtu0EdN5sw7Bvn507diRo9lZLd+ZS9Te25vc88ai23ljcW6bc8fiYiUHtRYL/9m8jtevn09ldQ2niwt5dcv6lutEC2rn6UNBWUNyXlhuJsDT54LJ7IWkF+cxMKwra0/uZ0CnGDxd3fF286C0qvWUa4T4+5JZ1LCPZBWZ6RcZ2qhNz7AgQgN8WX80mYXjBts6RGEHQb4+ZJsa9oucEjO9wkIv2n5Gv95sS0ptsrxnpxBcjQYy6hLe1qq8qBTPdg2TGp4B3pQXl+Lp792k7cZXv6EgLYfQHhF07t/wpeDkpkOk7TxOu4iO9Js5AjcvD5vE3txMBaX4t28YC79Ab0wFpfi2azoWy99chzIo4i+LYfTMgSilGDtrMEue+5adqw9RXVnDjY9eZcvw7cIJJnAdcga3G/Ca1joeKAF+D/xHaz1Ya90La5I77Zz2blrrQcArwKfAvVrrvsBEoLyuTT9gNtAbmK2UCucClFILlVK7lVK7z2zb1gJd+991Dw7GYtFc9/Zb3PDeO1w/YAChfn716z1cXXnyyit5bdNGyqqq7Bip/RgNBq7s0Yd7vljKvA//S0pBXl09rvP5/OBm4jqG8fiEuXTrGEZBmQmLbluzNkrBX2eM45nlG+wdinBQl/eKJz40mA92NK7vb+/jzaLpU3ny2zVoO8VmD2Puns70ZxZQW1NLzonTAHQd2YsrHp/P5Idm4+nnzf5lW+0cZcu75p6J/O7vc7jl8ZmcOnaGg5uPA3B4WyJ9R3fn/ldvZt6fr2TZaz+iLW18D1HN+HBQDjWDWydda332SFsC/AFIUUr9GfACArHO7H5b1+ZsgWI3IFNrvQtAa10C9T9f/6i1Lq57fgSIBNLP37DWejGwGGD8v/9fs+3deeZSgnx865938PElt7T0gm3yzGYMSuHt5k5JRQUTunVn16lUai0WisrLOXwmk7jgYDJLrBegPXnFNNYeP8bmpKTmCrdF5ZeW0vG8scg/byzOtskrbTwWFxPT3noxQWZJMQCbkhK5vv+gFoi+ZYyL6cPoLr0ASC3IJtDLB+rKTdt5+lD0P1wkVlRRymvbvwfA3ejKgLCulFe3ri8+WcUmQgMa9pGQAB+yihtm7nzc3YgLac8nd88GoKOvN/+9bSZ3vL2MQxnZNo9X2EaOyUywb8N+EeTnQ845M7pnXRYVwa0jLmPhh581KmfydnPjX7Ov5rUNWzl8JtMmMTe3xE2HSNmWAEC7iGDKCxvODeVFF569Pcvo6kJY7y6cOZhCSPdwPPwafoaPHt6DzW9+33KBt4Cdaw6xd90RADpFB1Gc3zAWJQWl+AY2HQu/QOssr7unG71HxHE6KYe+o7uzb/1RbnjEOm8WHhdCTXUtZaZyvP2lVKE1c8QZ3PMTSw28Blyrte4N/Bc493eUX1Noc+5v+rXYOLE/lp1FWEAAIX5+uBgMjI+NY3ty44R0W0oSk+PjARjTNZZ9Gdb8O8dkon9n64Szh4sL8aEhpBcUAvDghImcKijgi337bNibS3M8J4tO/gEE+1rHYkxMHDtSG4/FjtQkJsZZx2JUdCwHzjT5LtJIXqmZyHbt8few1tQN6BxBeqFjX2x3rvVJB3ly7VKeXLuUfWeSGB5p7Xt0YAhl1ZW/ujwBwMfNo/4L9RXdB7El9UgLRNyyDqZnEdWxHZ0D/XE1Griqf3fWHm7YR0wVVQx87DVGPf1fRj39X/alZUpy6wSOnMkiPDCATv7Wc8fkHt3ZdCK5UZtuwR159IqJ3P/ZcgrLyuuXuxgMvHjtdL4/eIQfjyXaOvRmEzu6N5MfnsPkh+cQ1qcLqTuPo7UmPyULVw+3JgludWVDXa6l1kJmQhq+wQEAjep1Mw4k4x8aaLN+NIfLJvfmrudnc9fzs+k+qAsHN1vHIiMxC3cvtyblCZZaC2Ul1n2itqaWE3tTCeps7bN/B19SDltntnNPF1BTVYOXX+uu0f4lTjCB65AzuBFKqWFa6+3APGALMBzIU0r5ANcCX1zgdceBUKXUYK31LqWULw0lCnZl0ZpXNqznhRkzMRoUKxMSSC0oYMGQoZzIyWFbSjIrEhJ4dPIUPrxpAaaKCp5atQKArw8e4KGJk3jnhhtBweojR0jOz6NXaCcmx/cgKS+XxXNvAODtbVv5KS3Vjj39ZRateW3Lep65ciYGpVhzPIG0wgJuHDSUxNwcdqQls+pYAn8eP4V35i7AVFnBcz+sqH/9+zfciperGy5GA8OiYvjL98s4VVjAkj07eHHGddRaask2mXhpfeu6c8BZB7NS6R0SxXNTb6aqtoZ3dv9Qv+7xifN4cu1SAK7tPYIh4d1wM7ry4hW3sjk1gW+O/ES3jp2Z1WsEGs2JvNN8tG+DnXry29VaNI9/9SMfLJyFwWDg852HSMzO576pIziUnsXahJ//tWLzX+/Ax8MNV6ORSb26ctObXzS5A0Nb8sKz8xk8MIaAAG/Wrvwbr76xmmXLL3xbtdasVmteXL2eV+bOwmhQfHPgMMl5+dw5ejhHM7PYlJjMHyaMxtPVlednWWfjsotN3P/5cib16MaAiDD8vTyY1rcnAE9+u5oT2U1vwddahPaMJPNIGisWLcHF1YXB8yfUr1vz/CdMfngOtZU1bFm8AktNLVprgmLDiBlp/bXo4PJtFGXkgVJ4B/oycM5YO/Xk0sX2jyRx/yle+eNHuLq7MOPO8fXr3nj4U+56fjY11bUsef47amtq0RZNl96dGTChBwCT5w/n2/9uYMeKA6Dg6t+Nr794ua1q490DQGntOHUm51xkthsYCBwBbgQeBeYCWcAJIE1r/UTdRWYPaK13171+MNZaXE+sye1ErAlx/W3ClFLfAf/QWm/4uVias0ShtXNzs3cEjiGsgxOcEX6ldduq7R2Cw/DZ0Dp/7m4J7rPC7B2Cw7h8sCPOH9lHfAf5OD1r3oB7HeKDZNy/Xm62/ynr773PIfp0Pkc8Amu01vPPW/bXukcjWuux5z3fBQw9r9l7dY+zbaYhhBBCCOGsnGAK1xETXCGEEEII0ULafnrrYAmu1joV6GXvOIQQQgghROvlUAmuEEIIIYRoYU4whSsJrhBCCCGEE3GC/NYh74MrhBBCCCHEbyYzuEIIIYQQTsQJbqIgCa4QQgghhFNxggRXShSEEEIIIUSbIjO4QgghhBBOxAkmcCXBFUIIIYRwJs5QgyslCkIIIYQQok2RBFcIIYQQQrQpUqIghBBCCOFEpERBCCGEEEKIVkZmcIUQQgghnIjM4AohhBBCCNHKSIIrhBBCCCHaFElwhRBCCCGciFLN9/jlbampSqnjSqmTSqmHL7DeXSn1ad36n5RSUc3RR6nBvYj8Qou9Q3AY910huwnA+9tq7R2CwwgJNdo7BIdRPSvM3iE4jMovT9s7BIeRERdh7xAcRkmlvSNwHPMG2DsCK1uV4CqljMCrwCQgA9illPpGa33knGa3AYVa665KqTnAC8DsS922zOAKIYQQQoiWcBlwUmudrLWuAj4BZpzXZgbwft3fXwATlLr0y+AkwRVCCCGEcCaqGR8/LwxIP+d5Rt2yC7bRWtcAxUD739Ktc0mCK4QQQgjhRJqzBlcptVAptfucx0J79w+kBlcIIYQQQvxGWuvFwOKLrD4NhJ/zvHPdsgu1yVBKuQD+QP6lxiUzuEIIIYQQTsR2FQrsAmKVUl2UUm7AHOCb89p8A9xc9/e1wDqttf7NnasjM7hCCCGEEM7ERv+Umda6Ril1D7AaMALvaK0TlFKLgN1a62+At4EPlVIngQKsSfAlkwRXCCGEEEK0CK31CmDFecseO+fvCuC65t6uJLhCCCGEEE7EVvfBtSdJcIUQQgghnIiNKhTsSi4yE0IIIYQQbYrM4AohhBBCOBGZwRVCCCGEEKKVkQRXCCGEEEK0KVKiIIQQQgjhRJyhREESXCGEEEIIJ+IE+a2UKAghhBBCiLZFZnCFEEIIIZyJE0zhSoIrhBBCCOFEnCC/lRIFIYQQQgjRtsgMrp0Mj4nioaljMRgMLNt7iHe27mq0/sahA5g5oDe1FguFpeU8/s1qMotNAIT4+fLE9EkE+/migXs+WsaZ4hI79KJ5aK354cPNJO1Pw9XdlWkLJxDSpWOTdh89vQxzURkubkYA5jw0HW9/Lw5uOsq6j7fh284bgIGT+tBvXA+b9uG3GhwZyT1jxmJUBr5POMzHuxvvB65GI49MnkJcUDAlFeU8uWIF2aYSXAwG7p8wkW5BwWiteWXjBg6czgDAxWDg3rHj6du5M1pr3t6+lU0nT9qje7/Z0KhI/jjBOi7fHDzMhzsbj0u/zmH8cfwYYjp25LFvV7D+RGL9umBfXx6ZOolgXx+0hvu//JqsktZ7fAyLjuKByWMxKANf7z/E+9sbj8UNlw1gRr+6c0VZOYu+W01WiYm44I48PHUC3u5uWCyad7b+xA9HT9ipF7ax6PHZjB4VT0GBmWuu/4e9w2lx8/qNoU9oFFU1Nby9aw1pRblN2lzTaxgjIuPxcnPnd8ter18e6OnL7ZdNwsvVHYMy8MWhrRzMSrVh9M3rmp5j6BEcRXVtDR/tX0NGceOxcDW6cMvAK+jg7Y9FaxKyUvj22FYAZvYcTdf2nQFwM7rg4+7FI6vesHkfbEnuouCAlFJ/AH4H7NVa32DveH4Lg1I8esV47vzwS7JLTCy94wY2HE8iOa+gvs2xrFzmLf6IipoarhvUh/smjubPX34PwNMzp/LW5p/YkXwKT1dXtNb26kqzSDqQRmFWMXe9NJ8zSdmsem8DC5687oJtp/9+EqHRQU2Wxw+NZcrNo1s61GZlUIp7x47nwWVfkWs28caceWxLTiKtoGE/uKJnT0yVlcx//13GxcVx58iRLFq5gmm9egNw20cfEuDpyQszZnLXJ0vRwPzLhlBYXsZNH7yHAnw9POzTwd/IoBR/mjSeez/7ihyTiXdunMfmpCRS8xvGJavExFMr13DD4IFNXv/YFVN4b8dOdqVZjw9LKz4+DErx0NTx3L3Ueq744NYb2JSYRMq554rsXL545yMqa2qYNaAPf5gwmkeXfU9FdTWPf7OK9MIiOvh4s+S2G9ienIa5stKOPWpZy7/dxcefbuGZRXPtHUqL6xMSRbBPAA+vfJ/owBBuHDCep9d92qTd/jMp/HjyAM9ffnOj5Vf1GMyu9ETWJx+ik28g942awYMr3rVV+M2qR1AUHX0CeHrd+0QGhHBd7/G8vKXpWKxL2svJ/AyMysDdw64hPiiSozlpLEvYVN9mVFRfOvs3nWBpa5whwW2NJQq/Byb9muRWKeWQCXyvsBDSC4o4XVRMjcXCqoRjjO0e06jNrtR0KmpqADiUkUmQnw8A0R0CcTEY2JF8CoDy6ur6dq1V4p4Ueo3shlKKsK4hVJZWYS4stXdYLa57cAhniovILLHuB+tOHGdEdOP9YER0DKuPHAFgY2IiA8IjAIgMDGRfejoAReXlmKsq6RYcDMDlPXqydNdOADRQUlFhox41jx6hIWQUFnGm2Doua48dZ3TXxuOSVVJCUm5ek+Q1qn0gRoOBXWkNx0dlKz4+enZqfK5Yc+QYY+Iaj8WetPT6Ph4+nUmwr/VccaqgiPTCIgDyzKUUlJbTzsvTpvHb2p69yRQXl9k7DJvo3ymabWlHAUguyMLLzR1/D68m7ZILsiiuuMCYaPB0dQOs/y0qN7dovC2pV0g0u9KtY5FWlIWnqzt+7o3Horq2hpP51l+5arWFjOIcAjx8mrzXwLA49p5u2790OAuHTAAvRin1BhANrFRKLQGuBjyAcuAWrfVxpdQC4BrABzAqpa4AXgF6Aa7AE1rr5XYIv16Qrw9ZJab65zklZnqHhV60/cz+vdl6MhWAyPbtMFVU8s/rryIswJ8dKaf419rNrXqWylRYil/7hhONb6A3psJSfOpKDs71/eIfUQZFt8ExjLh6EKrua+jxnUmkHztDYEgAE+ePwK+9r83i/606+PiQY2rYD3LNZuJDQhq38fYhx2xtY9Eac2Ulfh4eJOXlMTw6mh+PHyPI15e4oCCCfH3rE5pbhw2nb+fOnCku5t8b1lNY1no+9DueNy45JjM9Q0N+5hUNItq1w1xZyXMzptHJ359daad4bdOWVnt8BPn6kG1qfK7o9TPnihn9erMtKbXJ8p6dQnA1Gsio2z9E6xfg6UNBWUNSWlhmpp2nz4WT2Qv4+sgOHhg9kwld++Lu4sqLG5e1VKgtLsDDh6KKhrEoLjfj7+FDSeWFx8LTxY2ewdFsTNnfaHk7T18Cvfw5kZfekuEKG2lVM7ha67uAM8A44HVglNa6P/AY8Ow5TQcA12qtxwB/AdZprS+re92LSqmmmROglFqolNqtlNqdv3t7S3blV7uydzw9OgXz3rbdABgNBvpHhPHSmk3M++9HdA7wZ0a/nnaO0jam/34Stz8/l/l/u4b045kc3nIcgK79u/D7/3cTtz83hy69OvPdmz/aOdKWtyLhMLlmM2/Oncc9o8dyODOTWovGaFAE+fpyODOTOz9eypHMTO4a2bpKNy6F0WCgb+cwXtmwmVs/XEqnAH+u7NU66rEv1eW94okPDeaDHbsbLW/v482i6VN58ts1tM40X7SEIeHd2JJ6hD99/w4vb17OHUMmO8eV9Upx08DL2ZSyn/yyxrX5AzrFcSAzEe0ER4pSzfdwVK1qBvc8/sD7SqlYrL/Eup6z7get9dkitcnAdKXUA3XPPYAI4Oj5b6i1XgwsBuj75D9bbA/PMZkJ8WuYYQzyazxLc9aQLhHcPuoybnvvM6prawHILjFzPCuX00XFAKw/fpLenUNhX0tF2zL2/HCI/esTAAiNDqYkv+Hbt6mgtP6CsXP5Blpned093eg5PJYzyTn0HtUdL9+GGtO+43qw/hPH+HLyS/LMZoJ8G/aDjj4+5Jkb/0yYV2omyMeXPLMZg1L4uLvXlxy8tmljfbtXrptNRlEhJRUVlFdXs/mk9aKrDYknuKJnLxv0pvnknjcuQb4+5Jp/3c+nOSYTiTm5nCm2Hh+bEpPo1SmEbw8ltEisLS3HZCbYt/G5IucC54rLoiK4dcRlLPyw4VwB4O3mxr9mX81rG7Zy+EymTWIWLWd8TB/GRFuP55SCbAK9fCDfuq6dlw+F/0OZweguPfnn5q8BSCrIwtXggo+7J6bK8uYOu0WMjOrDsAjrWJwqym5UbuDv6UNxxYXHYnafCeSai5rM3gIMCIvj80MbWiBax+PAeWmzaVUzuOd5Clivte4FXIU1cT3r3AJOBczSWvere0RorZskt7aUcDqLiPYBhAX44WIwMLVndzYeT27UpntIR/42bSL3frKcgrKGE07CmSx8Pdzra+kuiwonOTffpvE3h4GTenPbs3O47dk5xA3swuEtx9Fac/pkFu5ebk3KEyy1FspM1nGoranl5L40OnYOBGhUr5u4J5X2ndrZriOX4Fh2FmEB7Qjxs+4H4+O6sS258X6wLTmZKT2sM5BjYmPr627dXVzwcLF+Px0YEUGtttRfnLY9OZl+ncMBGBAeQWpB69o/jmZmEd6uHaH+1nGZ2L0bm08m//ILgaNZ2fi4uxPgaT0+BkaEk3LOxWmtzZEzWYQHBtCpbiwm9+jOphONx6JbcEcevWIi93+2nMJzzhUuBgMvXjud7w8e4cdjiee/tWiF1iUd5PEflvL4D0vZezqJ4ZHxAEQHhlBeXfmryxMA8stMxAdZzxOhvu1wNRpbTXILsCX1IC9uWsqLm5ZyKCuJweHWsYgMCKGiuvKC5QlXdBuGp6s7yxI2NlkX5NMOT1cPUgvli2Bb0dpncE/X/b3gZ9qtBv5PKfV/WmutlOqvtbbrfGet1jy3Yj2vz5+FQSm+3n+YpNx8fj92OAlnsth4Ipn7Jo3Gy82VF6+bBkBWsYl7P1mORWv++cNGFt90LQrFkcxsvtxzyJ7duWQx/SJJOpDGG39agqubC1cunFC/7u1HP+G2Z+dQU13Lpy98S22tBW2xENUzvP5WYLvXHCRxbwoGowEPbw+m3TnhYptyKBat+feGdfz96mswKMXKIwmkFuRzy9BhHM/OZltKMt8nHObRKVNZcvMtlFRU8NTKFQAEeHrx95kz0VqTZy7ludWr6t938dbNPDJlKne7j6G4vJwXflhjry7+JrVa89Ladfy/a6/BYFB8dyiBlPx87hgxjKNZ2WxJSiY+JJjnr74KX3cPRsZEc/uIYdzw7gdYtOaVDZt4ZfYsFIpj2dksP9B6j49arXlx9XpemTsLo0HxzYHDJOflc+fo4RzNzGJTYjJ/mDAaT1dXnp9lPVdkF5u4//PlTOrRjQERYfh7eTCtr7WM6clvV3Miu+mtpNqKF56dz+CBMQQEeLN25d949Y3VLFu+095htYiDWan0CY3ihctvpqq2hrd3/VC/7slJ83j8h6UAXNd7BEMjuuFmdOWlK29lU0oCy4/8xKcHNrNg0AQmx/UHaPT61uZITio9gqL423jrWCzd39CXB0fP48VNS/H38GFK3GVkmQp4YPQ8ADanHmDHKeuvOwM6xbHPiS4uc+TSguaiWtstppRSqcAgIBZ4H+ts7ffAfK11VN1FZoO01vfUtfcE/h8wHOuMdYrWetovbaclSxRam/uuaM3fg5rP+9tqf7mRk6iosncEjqO6Sk4VZ1V+efqXGzmJQY9E2DsEh+HXuu5U2KL+ddW9DpFazvnoX8124vrkBsfo0/laXeaitY6q+zMPiDtn1V/r1r8HvHdO+3LgTttEJ4QQQggh7K011+AKIYQQQgjRRKubwRVCCCGEEL+dM9TgygyuEEIIIYRoU2QGVwghhBDCiTjBBK4kuEIIIYQQzkRKFIQQQgghhGhlZAZXCCGEEMKJOMEEriS4QgghhBBOxQkyXClREEIIIYQQbYrM4AohhBBCOBFnuMhMElwhhBBCCCfiBPmtlCgIIYQQQoi2RWZwhRBCCCGciJQoCCGEEEKINsUJ8lspURBCCCGEEG2LzOAKIYQQQjgRKVEQQgghhBBtihPkt1KiIIQQQgghbE8pFaiU+kEplVj333YXaNNPKbVdKZWglDqolJr9a95bZnAv4v4rZWjOem9rrb1DcAh5BRZ7h+AwxgyU4+Osdh7OMBfy62TERdg7BIex+7lT9g7BYZSOC7V3CA7jX1fZOwIrBypReBj4UWv9vFLq4brnD53Xpgy4SWudqJTqBOxRSq3WWhf93BvLDK4QQgghhDNRzfi4NDOA9+v+fh+4+vwGWusTWuvEur/PADlAx196Y0lwhRBCCCGEPQRrrTPr/s4Cgn+usVLqMsANSPqlN5bfGYUQQgghnEhzVigopRYCC89ZtFhrvfic9WuBkAu89C/nPtFaa6WU/pnthAIfAjdrrX+xZlASXCGEEEIIJ9KcNbh1yezin1k/8eJxqGylVKjWOrMugc25SDs/4HvgL1rrHb8mLilREEIIIYQQ9vANcHPd3zcDy89voJRyA5YBH2itv/i1bywJrhBCCCGEE3Gca8x4HpiklEoEJtY9Ryk1SCn1Vl2b64HRwAKl1P66R79femMpURBCCCGEcCKOcpswrXU+MOECy3cDt9f9vQRY8r++t8zgCiGEEEKINkVmcIUQQgghnIiDTOC2KElwhRBCCCGciKOUKLQkKVEQQgghhBBtiszgCiGEEEI4EWeYwZUEVwghhBDCiThBfislCkIIIYQQom2RGVwhhBBCCCciJQpCCCGEEKJNcYL8VkoUhBBCCCFE2yIzuEIIIYQQTkRKFESL0VrzwwebSTqQhoubK1fdOYGQLh2btFvy9DLMRWW4uBoBmPvwdLz9vQA4siORzV/uQikIiujA1fdMtmkffqvBkZHcM2YsRmXg+4TDfLx7V6P1rkYjj0yeQlxQMCUV5Ty5YgXZphJcDAbunzCRbkHBaK15ZeMGDpzOaPTap6+aTic/f2796ENbdqnZjOgaxUOXj8OoFF/tPczbW3Y2Wn/TsIFcM6A3tRYLBWVlPPb1ajKLTQDcN2k0o+O6YFCK7UlpPL9yvT260Gyu7TWGnsFRVNXW8OG+NWQU5zZa72p04bZBV9DByx+tNYeyU/jm6FYAhoTHc3WPkRRXlAKwMeUA208l2LwPzUVrzb4vN5OVkIbRzZXL5k+gXXjT88Wm176lvLgUbbHQIaYTA64fjcFg4PCKnaRsO4K7jwcAva8aSmjPKBv3onnM6zeGPqFRVNXU8PauNaQV5TZpc02vYYyIjMfLzZ3fLXu9fnmgpy+3XzYJL1d3DMrAF4e2cjAr1YbR286ix2czelQ8BQVmrrn+H/YOx6ZGd4visavHYzAoPvvpEG+s23nBdlN7x/LaghnMePlDDmVk2zhK+3KC/FYSXHtJOpBGQVYxd700nzMns1n17gYWLLrugm1n/H4SodFBjZYVZBWx/Zu93PTENXh6e1BaXGaLsC+ZQSnuHTueB5d9Ra7ZxBtz5rEtOYm0goL6Nlf07ImpspL577/LuLg47hw5kkUrVzCtV28AbvvoQwI8PXlhxkzu+mQpuu51o2K6UlFdbYdeNQ+DUvzlygks/OALskpMfLLwBtYfP0lybsPYHM3MYc7iJVRU13D94L7cP3kMD37+HX3DO9E/ohOzXvsAgA9um8OgqM7sTs242OYcWo+gKDp6B/Dkj+8T1S6EOX3G84/NnzZp9+PJvSTmZ2BUBv5v+DX0CIrkSE4aAHvPJPL5oQ22DbyFZB1Jw5xTzOWPzacgNZs9n25g4gNNzxfDbpmCq6cbWmu2vb2KjH1JRAyMBSB2XF+6T+hv69CbVZ+QKIJ9Anh45ftEB4Zw44DxPL2u6X6x/0wKP548wPOX39xo+VU9BrMrPZH1yYfo5BvIfaNm8OCKd20Vvk0t/3YXH3+6hWcWzbV3KDZlUIonr5nITW9+Tlaxia//OJ+1CUmczM5v1M7b3ZUFowawL+2MnSIVLa1ZanCVUguUUv/5hTZRSqnDdX/3U0pd0Rzb/pntvaeUurYlt3EpTuxJofeobiilCIsNoaKsCnNh6a9+/f51Rxg4qTee3tYZmbOzuo6ue3AIZ4qLyCwppsZiYd2J44yIjmnUZkR0DKuPHAFgY2IiA8IjAIgMDGRfejoAReXlmKsq6RYcDICHqyvXDRjAhzt/smFvmlfvsBBOFRSRUVhMTa2FlYePM65710ZtdqWmU1FdA8DB9EyC/XysK7TG3cUFV6MRNxcjLgYD+ebW8aXnQvqERLMz4ygAqYVZeLq64+feeB+vrq0hMd+awNdqC+nFOQR4+Ng8Vls4fSiFqMus54v2XUKoLq+ivLjp+cLV0w0AbbFgqbXYOswW179TNNvSrPtFckEWXm7u+Hs0PfclF2RRXHGB/V+Dp6t1jDxd3SgqN7dovPa0Z28yxa1k4qM59Y0IIS2/kPSCYqprLXy37xiTesY0aXf/1JG8uX4XldW1dojS/pRqvoejstcMbj9gELDCTtu3O3NBKX7tGz6MfQO9MRWW4tPOu0nb7978EWVQdL8shhFXD0IpRUFWEQAfPPElFotm1KzBxPSNtFX4v1kHHx9yTKb657lmM/EhIY3bePuQY7a2sWiNubISPw8PkvLyGB4dzY/HjxHk60tcUBBBvr4cy87m1mHD+WzvnvrkrzUK8vMhq7hhbLKLTfTpHHrR9tcM6MWWxBQADmRksjMlnXUP3IlSio937iMlr+Cir3V0AR4+FJ6TfBSVmwnw8KGk8sIf2J4ubvQOjmZD8v76Zf1Cu9K1fRg55kK+PLyJoorWm8yUF5Xi2a7hfOEZ4E15cSme/k3PFxtf/YaCtBxCe0TQuX/DB/vJTYdI23mcdhEd6TdzBG5eHjaJvTkFePpQUNbw/7GwzEw7T58LJ7MX8PWRHTwweiYTuvbF3cWVFzcua6lQhZ2E+PuSWdRwHs0sNtMvovF5tGdYEKEBvqw/mswdYwfbOkSH4MB5abP5VTO4SqmvlVJ7lFIJSqmFdctuUUqdUErtBEac07bRzKlSynzee7kBi4DZSqn9SqnZF9nmE0qp95VSm5VSaUqpa5RSf1dKHVJKrVJKuda1e0wptUspdVgptVippt8nlFIDlVIb6/qwWil18azBwcz4/STueGEuNz52DenHMjm85TgAlloLBdlF3PDXq7n6nsmseGsDFaWVdo62Za1IOEyu2cybc+dxz+ixHM7MpNaiienQkU7+/mxJSrJ3iDYzrU88PToF8+7W3QCEBwYQ3TGQif9czISX3mRIlwgGRITZOUrbMCjFgoGXsyFlP/llJQAczkrh8bXv8tyGjziWe4ob+7eO+vTmMObu6Ux/ZgG1NbXknDgNQNeRvbji8flMfmg2nn7e7F+21c5R2seQ8G5sST3Cn75/h5c3L+eOIZOd4oNeNFAK/jJ9HM98s8HeoYgW9mtncG/VWhcopTyBXUqp74EngYFAMbAe2Pdr3khrXaWUegwYpLW+5xeaxwDjgB7AdmCW1vrPSqllwJXA18B/tNaLAJRSHwLTgG/PvkFdIvwKMENrnVuXUD8D3Hr+xuqS94UACx6Zzdhrhv+aLv1qu9ccYv9664UunaKDKclvyP1NBaX4XmD21jfQOmvj7ulGz+GxnEnKofeo7vgG+tCpazBGFyMBQX4EhvpTkFVEp5jgZo25ueWZzQT5+tY/7+jjQ5658cxaXqmZIB9f8sxmDErh4+5OSUUFAK9t2ljf7pXrZpNRVEjfsM50Cwrm41tuxagMBHh58fKsa7nvyy9s06lmklNiJsS/YWyC/X3JNjWddRwaHcEdo4dwy7ufUl1r/XltQnxXDmZkUl5lrUHekphC3/BO7D112jbBN4PRUX0YHtkLgLSibNp5NsxYBnj6XHQGdm7fCeSWFjWavS2trqj/e1taAlf3GNkyQbegxE2HSNlmPV+0iwimvLCh/+VFF569Pcvo6kJY7y6cOZhCSPdwPPwafsaPHt6DzW9+33KBN7PxMX0YE23dL1IKsgn08oG6csp2Xo1n+n/J6C49+efmrwFIKsjC1eCCj7snpsry5g5b2ElWsYnQgIbzaKi/D9nn/DLm4+5GXGh7Pv69dW6to683i2+dycJ3ljnVhWaOXFrQXH5tgvsHpdTMur/DgRuBDVrrXACl1KdAXAvEt1JrXa2UOgQYgVV1yw8BUXV/j1NK/RnwAgKBBM5JcIFuQC/gh7rJXSOQeaGNaa0XA4sB3t/9b32hNpdi0OTeDJpsvVDq5L5Udq85RI9hsZw5mY27p1uT8gRLrYWKskq8fD2praklcV8aXXp1BiBuUBeObE+k75h4ykzlFGQWExDk39whN7tj2VmEBbQjxM+PPLOZ8XHdeHrVykZttiUnM6VHD45kZTImNra+7tbdxQUFVNTUMDAiglptIa2ggLSCAr45dBCAYF8/nps+o9UltwCHz2QRGRhAWIAf2SYzl/fqxkNfNK7i6R4SxGNXTeKuD7+koLThQzmzyMS1A3vzlkGhUAyM6syS7Xtt3YVLsin1IJtSrf8fewZFMbpLX/acPkFUuxDKqysvWJ4wrfswPF3cWbp/baPlfu5e9e17h0STZWp95Rqxo3sTO9p6vjhzOJWTmw4RPjCWgtRsXD3cmiS41ZVV1FRU4+nvjaXWQmZCGh1irD9WnVvOkHEgGf/QQNt25hKsSzrIuiTrftEnJIoJXfvyU/oJogOt+8WvLU8AyC8zER8Uzta0o4T6tsPVaJTkto05mJ5FVId2dA70J7vYxLT+3fnjkoYvdKaKKgY99lr986W/m81z325wquQWnKNE4RcTXKXUWGAiMExrXaaU2gAcwzqreiE11JU+KKUMgNslxFcJoLW2KKWqtdZnk04L4KKU8gBewzobnK6UegI4v7BMAQla62GXEEezi+kXycn9abx+/xJc3VyYdueE+nVvPfIJtz83h5rqWj55/ltqay1oi4WoXuH0G28d9ug+EaQcSufNB5diMCjGzxuOl6/j19RZtObfG9bx96uvwaAUK48kkFqQzy1Dh3E8O5ttKcl8n3CYR6dMZcnNt1BSUcFTK61JXoCnF3+fOROtNXnmUp5bveoXtta61Fo0z65Yxxs3zsJoMLBs32GScvO5e9xwEs5ks+F4En+aPBovN1deuv4qADKLTfzh46/54cgJhkSH89Xvb0Zr2HoyhY0nku3co98uISeVnsFRPD7hZqpra1iy74f6dQ+PmcfzG5cS4OHD1LjLyDIV8NCYeUDD7cDGRvejd3A0tdpCWXUFS/b/cLFNtQqhPSPJPJLGikVLcHF1YfD8hvPFmuc/YfLDc6itrGHL4hVYamrRWhMUG0bMSOvM58Hl2yjKyAOl8A70ZeCcsXbqyaU5mJVKn9AoXrj8Zqpqa3h7V8P/1ycnzePxH5YCcF3vEQyN6Iab0ZWXrryVTSkJLD/yE58e2MyCQROYHGe9m8S5r29rXnh2PoMHxhAQ4M3alX/j1TdWs2z5hW+X1ZbUWjRPfPUj7y+chUEZ+HznIRKz8/njlBEcysjixwTnKWVzdqohZ7xIA6VmALdrra9SSnUH9gO3AM8DA4ASYB1wQGt9j1Lqr4Cv1vohpdTVwDKttVJKRQHfaa17KaVmAdO11jdfYJNnt/sEYNZa/6PuuVlr7XPuOuAt4DjW2VwjsAP4Qmv9hFLqPeA74BvgCHCj1np7XclCnNb6Z2+K2RIzuK3Ve1ud8yrT8+UVtL2r0n+rMQPlDoNntfNwhrmQXyejWE6bZ+1+7pS9Q3AYpeNazWUvLS75pQcc4oTxwIp/NdvB+o8r7nWIPp3v11xktgrrbOlRrEntDqw/8T+BtS52K3D0nPb/BcYopQ4Aw4AL3ftqPdDj5y4y+zW01kV12zsMrAZ2XaBNFXAt8EJdTPuB5i2uFUIIIYRoJeQ2YYDWuhK4/AKrNgBN7pCttc4Ghp6z6KG65alYa2HRWhcAP3tvDq31E+c997nQOq31X4G/XuD1C875ez8w+ue2J4QQQgjhDBw4L202zfIPPQghhBBCCOEo7F5Ip5S6Bbj3vMVbtdZ32yMeIYQQQoi27AL/ZECbY/cEV2v9LhcodRBCCCGEEM2v7ae3UqIghBBCCCHaGLvP4AohhBBCCNtxggoFSXCFEEIIIZyJE+S3UqIghBBCCCHaFpnBFUIIIYRwIgYnmMKVBFcIIYQQwok4QX4rJQpCCCGEEKJtkRlcIYQQQggnIndREEIIIYQQbYoT5LdSoiCEEEIIIdoWmcEVQgghhHAiUqIghBBCCCHaFCfIb6VEQQghhBBCtC0ygyuEEEII4USkRMGJbU7T9g7BYVTX2DsCx+Dp4QRnhF9peGd7R+BI5FxxVkmlvSNwHKXjQu0dgsPwXp9p7xDEeRzl00wpFQh8CkQBqcD1WuvCi7T14/+3d+fxVdT3/sdfn8gWCARQdpUIZZFdNgUBBXHrtSpgS11qsQu1atHr9bZ6+6iXqtda26ut9VqL/rxSxYobgksRZVHwqiB7kB2CgISdkLCTfH5/zIRzkpwQliTnJOf9fDzOI98z85053/nOzPd85jvfOYGvgLfd/a6y1q0hCiIiIiISD/cD0929HTA9fF+ah4FPTnTFCnBFREREkkiKld/rNF0HjA/T44HrY2Uys15AM2Daia5YAa6IiIhIErFyfJ2mZu5eOIYlmyCILVpWsxTgv4H7TmbFGoMrIiIiIqfEzEYDo6MmjXP3cVHzPwKax1j019Fv3N3NLNZDDXcA77v7JjuJp+MU4IqIiIgkkfL8FYUwmB13nPlDSy+HbTWzFu6+xcxaANtiZOsHDDSzO4A0oJaZ5bn78cbrKsAVERERSSaJ8isKwBTgh8Bj4d/JxTO4+82FaTMbBfQuK7gFjcEVERERkfh4DLjczFYDQ8P3mFlvM3v+dFasHlwRERGRJJIo/+jB3XcCl8WY/iXwkxjTXwRePJF1K8AVERERSSIJEt9WKA1REBEREZFqRT24IiIiIkkkUYYoVCQFuCIiIiJJJBkCXA1REBEREZFqRT24IiIiIkkkGXo3FeCKiIiIJBENURARERERqWLUgysiIiKSRJKgA1cBroiIiEgy0RAFEREREZEqRj24CeLG7pfQtXkGh/OP8sKX0/h6z/Yi82udUYPbL/w2TdLSKXBnyZb1vJn5aZxKW74uzGjN3YMvJcVSeDczk5fnzisyv3urVowZfAltmzRh7LvvM2v16iLz69aqxcujbmX2mrU8OWNmJZa8Yl3UJoN/G3opKSkpTF60lL9/XrReburTk2t7dCW/oIA9+w/w8HsfkL03N06lLX/uztTxc1i9aAM1a9Xg+p9fRovzmpTI9+JDb5O3Zz81ap0BwA8e+A710uuSsyOXt/86nYP7DlNQUMDQG/vR7oLWlb0Z5UJ1ETG88yV0apbBkfyjTFg0jU05RdvKmmfU4LZe3+asekFbuSx7Pe+sCNrKYZ0H8a0zzwaCNjWtdl0emPpspW9DRRjUIYMHrx9CSorx2hdLeXbG3Jj5rurajmdGXcd1T77E0k1bK7mU8fHQf45k0MDz2bUrj+Hf+2O8i5MQkqADt+oEuGaWBfR29x3xLkt569o8g6ZpDfmPD8bTpnFzbrlgCI/OnFgi3werF7By+ybOsBT+bdBwujRrTebWDXEocflJMePey4bwr2+8xbbcXJ6/+SbmrFlL1q5dx/Jszc3l0anTuLF3r5jr+OnF/Vm8aXNlFblSpJjxyyuGcNerb7Jtby7jR93M7NVrWb8zUi8rt27nh/87gUNHjzLigm78YvAgfj35vTiWunytWfQ1u7Jz+MWTN7N5zVbe+38f85NHboiZd/idQ2nZtmmRaZ9Mmk+ni75Fn8u7sH3TLib8/j3u+csPKqPo5U51EejUNIMmaQ15ZMZ4Wjdszne7DuHJOSXbyhlrF7BmZ9BW3tlvOOc3bc3ybRuYtOyTY3kGZnTn7PSSFwlVUYoZvx0+lFv/9jrZObm8fc8tfLRsLWu27iySr17tmowa2JOFG76JU0njY/I78/jHxDn810M3xrsoCUNDFKRS9GjRhs82LAdg3a5s6tasTXqdukXyHM4/ysrtmwDI9wK+3rONRqlplV7W8nZ+8+Zs2rOHb3JyOFpQwEcrVzLgW22L5Mneu5e1O3ZQ4F5i+Q5Nm9Kobl3mbqjagX5xnVs2Z9PuPXyzJ6iXactXMKh90XqZ//VGDh09CsDSb7bQtEHVPx6irZi/nm4DO2BmnN2uOQf3HyZ3974TX4HBoQOHATi4/zD1G9UtY4HEpboIdGnehnkbg7Zyw55sUmvWpkHtottyJP8oa3ZG2spNOdtoWKfkudGrVXsWbF5V8YWuBN3Pbc6GnbvZuCuHI/kFvLtwBZd3blsi371XDeBvM+dx6Eh+HEoZP/MXrCMnZ3+8iyGVLCEDXDN728zmm9kyMxsdY/69ZpYZvu4Jp2WY2XIzey5cbpqZpYbz2prZ1HCds82sYyVv0nE1TE1j14G8Y+93H8iL2SAXSq1Zi+4t2rB8+8bKKF6FapKWxrbcyG317bl5NEk7sUDNgLsuHcT/fPxJmXmrmiZpaWyNGm6wLTePJvXrl5r/2u5d+WxtViWUrPLk7tpH+pmRY6FB43rk7ood1E3+2wyevX8iH7/1JR5eCF06og9L56ziiTvH88rj73H1qIGVUu6KoLoINKyTxp6DkbYy50Ae6cdrK2vUonOzNqzaUbStbJRan8Z100tMr6qap9dny55Ie7ElJ49m6UXbi86tmtKiYX1mLl9X2cWTBGTl+EpUiTpE4UfuvisMUOeZ2ZuFM8ysF3AbcCFB3X5hZh8Du4F2wI3u/lMzew0YAbwMjANud/fVZnYh8AwwpPiHhsH0aICLf/Y9Ol7ev0I38lSkmDG679VMX7OIHfv2xrs4cTWsR3c+W5/F9ry8sjNXY1d1Pp/zmzfj9gmvxbsocTH8rqE0aJzGoQOHee3JqSyZnUb3QR3J/L/VdB/Ukf7X9GDjqmwmPTOdOx7/PpaSyE3y6VFdRKSYcWuvq/lk/SJ27i/aVvZs2Z7FW1bjlLwrVB2Zwa+vHcy/v/rPeBdFEkQyDFFI1AB3jJkNC9PnEASuhQYAk9x9H4CZvQUMBKYA6919UZhvPpBhZmlAf+B1i+zR2rE+1N3HEQTD/OTNP1doyze4TTcGntcFgKzdW2kcNdygUWrRXopot/a8jG15e/hozaKKLF6l2Z6XR9Oonskm9dNOOGDt0rIF3Vu1Ylj3bqTWqkXNlBQOHDnCs7PnVFRxK832vDyaNYjUS9P6aWzPLfkAWZ+Mc7mtf19un/AaR/Kr/m3HudOWsmDGVwC0bNOUnJ2RY2Hvrn3Ub1yvxDINGgfnTu3UWnS9uD2b126j+6COLJy5nJsfuAaAc9o35+iRfPbnHqBeetW4Pa+6CAzI6Ea/c4O28us9W4vc3UpPTSOnlLZyZLfL2J63h4/XLyoxr2er9ry+dFYFlDY+snNyadEw0l60SE9ja06kvUirXYv2Lc7kH3eMBKBJ/XqM+9EwRr8wKWkeNJPkk3ABrpldCgwF+rn7fjObBdQ5wcUPRaXzgVSCYRh73L1H+ZXy9M1ct4SZ65YAwUNmQ9p2Z+6mVbRp3JwDRw6Rc7DkeKHrO/UjtWZtxs//qLKLW2FWZGdzTsNGtGjQgO15eQzt0IHfvn9ivQwPvT/1WPrqzp3o2KxZtQhuAb76JptzGjWkZXoDtuXmccX5HfnNlPeL5GnfrAkPXDWUuye+xe79B+JU0vLV94qu9L2iKwCrFmQxb1omXfp/i81rtlK7bi3qNyoa1BXkF3Bw3yHqNkgl/2g+qxZk0aZL8JR8+ln1WZ+5mR6XdGT75l0cPXyUug1SK32bTpXqIjAnawlzsoK2slPTDAae150F36yidcPmHDxyiL2HSraV3+4QtJWvLi7ZVjZNa0RqzTpk7d5S4WWvLEs2ZpNxViPObpzO1pxcrrmgI/e8HHngNPfgYXo/+Myx96/8fCS/e2eWgtsklgQduIkX4ALpwO4wuO0IXFRs/mzgRTN7jGAfDQNKfRzY3fea2Xoz+667v25BN243d19cURtwspZmZ9G1eQaPXvlDDucf5X+//PDYvAcvu4mHpr9Co9Q0rjm/L1v27uI3l90EwMy1i5mdtSxexS4X+e48MWMGT4wYTkqK8V7mMtbv3MmP+/djxdatfLp2HR2bNePR675D/Tp1uLhtG37cvx8/GP/3eBe9QuW784cPZ/LU90eQYsY7SzJZt2Mnowf2Z/mWbGavWceYwYNIrVWT3w0Leuay9+Zy3xuT41zy8tPugtasXvQ1f7lnAjVr1+C6n0VGFT17/0Ruf2wkR4/k8/Jj75J/NB8vcM7rejY9L+sEwBW39Oed52bx+fuLweD6nw/Bquh9OdVF4KttWXRqmsFvhgRt5SuLIm3lvw+6iT988grpddK4sn1fsnN3cd+goK2cnbWYz78O2sqeLduzsJo8XFYov8AZ+9Z0xo8eQYql8PrcpazeupN7rryYpZuymb5sbbyLGFe/f/QW+vRqS8OG9fjon7/hf579gEmTY/+MWrKogqf/STOP8WR6PJlZbeBtIANYCTQExgIvEv5MmJndC/woXOR5d/+TmWUA77p7l3A99wFp7j7WzM4D/gq0AGoCr7r7Q8crR0UPUahKVmQVxLsICeHwYR0She658ox4F0ES0BfV69f6Tss7s47EuwgJo97M6tNbfrqWLvjvhAgt//rZU+X2hfbzfmMSYpuKS7geXHc/BFwdY1ZGVJ4ngCeKLZcFdIl6/8eo9HrgqnIuqoiIiEiVk5A/oVXOEi7AFREREZGKkwxDFJIhiBcRERGRJKIeXBEREZEkkgQduApwRURERJKJhiiIiIiIiFQx6sEVERERSSJJ0IGrAFdEREQkmSTDEAUFuCIiIiJJJBkCXI3BFREREZFqRT24IiIiIkkkCTpwFeCKiIiIJBMNURARERERqWLUgysiIiKSRJKhd1MBroiIiEgS0RAFEREREZEqRj24IiIiIknE8HgXocIpwBURERFJIhqiICIiIiJSxZh79e+mrqrMbLS7j4t3ORKB6iJCdRGhuohQXUSoLiJUFxGqi+SiHtzENjreBUggqosI1UWE6iJCdRGhuohQXUSoLpKIAlwRERERqVYU4IqIiIhItaIAN7FprFCE6iJCdRGhuohQXUSoLiJUFxGqiySih8xEREREpFpRD66IiIiIVCsKcEVERESkWlGAm8DM7Fozu7+c1pVXHuupLGZ2qZm9G+9ySPyZ2fNm1ilMV6njOJGY2Sgzezre5ThdZjbGzJab2YR4l0USj5llmdlZ8S6HxJ8C3Dgzs1L/XbK7T3H3xyqzPCKJxt1/4u5fxbscVcXx2pRq4g7gcne/uayMVbkuTuSCxMwyzCwzTPcws29XcJleNLMbKvIzRMqLAtxyYmb1zOw9M1tsZplmNjL6StLMepvZrDA91sxeMrNPgZfM7HMz6xy1rllh/lFm9rSZpZvZBjNLifqsjWZW08zamtlUM5tvZrPNrGOY5zwz+8zMlprZI5VfIyWFjfEKM5sQ9sC8YWZ1zayPmf1fWHdzzax+seX6htuyMMzXIZzeOcy/yMyWmFm7WPshPlt76szs7XB/LjOz0eG0H5vZqnB7nyv84jOzJmb2ppnNC18Xx7f0p6eU82iWmfWOyvNkWDfTzaxJOG2MmX0VHgevhtMKz7PPzGy1mf00Xtt1so5zrjwY7udMMxtnFvxH+bCO/mRmXwJ3H+ecahm2F6vN7PH4beGpMbNngTbAP83sV6W0C6PMbIqZzQCmh8fUC2E9LDSz6+K6ERWnB1ChAW6iidVWFpt/b3iuZJrZPeG0jPCcei5cbpqZpYbzYn6fShXl7nqVwwsYATwX9T4dyALOCt/3BmaF6bHAfCA1fP+vwG/DdAtgZZgeBTwdpicDg8P0SOD5MD0daBemLwRmhOkpwK1h+k4gLwHqKANw4OLw/QvAL4F1QJ9wWgOgBnAp8G70tDA9FHgzTP8FuDlM1wJSY+2HeG/3KdRT4/BvKpAJtAqPpcZATWB21HHxCjAgTJ8LLI93+U9z22OdR7OA3uF7j9rnD0bVwzdA7TDdMPw7Flgc1uNZwEagZby38QTrIda5cl/hsRFOewn4TpieBTwTpmuVck6NCqenA3WADcA58d7WU6ibrHB/ltYujAI2RZ1HjwK3FB4bwCqgXgJsx9sE3wPLgNHhtNvC8s0Fnos6vl8EbohaNi/qOMkM9/nXwHZgETCylM8cC4wP25ANwHDgcWApMBWoGeZ7EJgXrnsckV9cOlYOoBfwcbgNHwAt4lCHxdvKM6OOj17hdtUD0sJ6viCss6NAj3DZ16KOj5jfp3pVzZd6cMvPUuByM/u9mQ1095wy8k9x9wNh+jWg8LbP94A3YuSfSBDYAnwfmGhmaUB/4HUzWwT8jSBABrgY+EeYfulkN6YCbXT3T8P0y8CVwBZ3nwfg7nvd/WixZdIJtjETeBIo7O3+DPgPM/sV0Dqsz5PdD4lojJktBj4HzgF+AHzs7rvc/QjwelTeocDT4f6fAjQIj4uqqqz9V0BwLkBw/AwI00uACWZ2C8GXV6HJ7n7A3XcAM4G+FVj28lb8XBkADDazL8xsKTCEyLkAkXrpQOnn1HR3z3H3g8BXQOsK34qKU1q7APChu+8K01cA94fnyCyC4P7cSixnaX7k7r0IOj/GmFkr4LcEbfcAoNOJrsjdDxMEpRPdvYe7TzxO9rYEx861BMfVTHfvChwA/iXM87S793H3LgTB4zXRKzCzmgQdDDeE2/AC8F8nWt5yVLytbBc1bwAwyd33uXse8BYwMJy33t0Xhen5QEYZ36dSBVXZ8UmJxt1XmVlPgltEj5jZdIIv2sKLiDrFFtkXtexmM9tpZt0IgtjbY3zEFOBRM2tMcGU6g+DKdI+79yitWKe6PRWoeJn2UrJuinuYoBEeZmYZBF9SuPsrZvYFQaP8vpn9zN1nFN8P7v5QuW5BBTKzSwmC1n7uvt+CYS0rgPNLWSQFuCgMWKq8Us6j4y4S/v0XYBDwHeDXZta12Pzi+auCWGV/hqA3e6OZjaXoubOPsh2KSudTtb8DYrYLoei6MGCEu6+sxLKdiDFmNixMF17IznL37QBmNhFoXwGf+093PxJeJJ1B0HMLwcVlRpgebGa/BOoS3DlaBrwTtY4OQBfgw3CUzBnAlgooa6lKaSvL+i4pVPw8SCVoS4/3fSpVjHpwy4mZtQT2u/vLwB+AngS3SnqFWUaUsYqJBLfr0919SfGZ4RXoPODPBLfu8919L7DezL4blsHMrHu4yKcEPb0AZT6MUYnONbN+YfomgivvFmbWB8DM6lvJB0PSgc1helThRDNrA6xz96cIhnB0K2U/VCXpwO6wwe4IXERwIXOJmTUK6yb6WJoG/KLwjZn1qMzClrcT2H8pRO523ATMsWBs+jnuPhP4FUEdFvZiX2dmdczsTIJhL/MqeBPKU/FzZU6Y3hH2NpX2sM9Kyj6nqoOY7UIMHwC/iBqvfEEFl6tMxYKz7sBCggvZ0hzrLAmP91qn8fGHANy9ADji7oUXUgVADTOrQ3AhdUPYs/scJQNHA5aFvcU93L2ru19xGmU6FbHaymizgestGLteDxgWToupjO9TqYIU4JafrsDc8NbGfwKPENxu+rMFD37kl7H8GwQB6WvHyTMRuIXIrUgIgtcfh7dplgGFD1DcDdwZXqW3OrlNqVArCcq1HGhEcJtrJPCXcBs+pGRj+jjwOzNbSNEep+8BmWGddwH+Tuz9UJVMJfiSWQ48RnABsJlgHOFcgguXLKDw1v0YoLcFD1d9Reze/6qkrP23D+gb3pYeAjxE0Hv0cnisLwSecvc9Yf4lBEMTPgcedvdvKnwLyk/xc+WvBMFGJkHQFjNYD29Xl3VOVQeltQvFPUwwdn2JmS0L38dbrOAsleBC9sxwCMB3o/JnEeksuZZge4rLBerHmH6yCo+V411IrQSaFF6AWfDAc+cY+SpSrLbyGHdfQDBmeC7wBcFzKwvLWGdp36dSBelf9UqlCW8jvhuO65KTYGZp7p4X9sRNAl5w90nxLlciC2/h57n7H+NdlpOlc6V6M7PaBA+ZZRAEiw0JHgA7D3gA2EPwsNhhd7/LzJoR3KVKJQjs7nT3tOjjJBy+9gFB8Pu7WONwi58TZpbn7mnF51nwyzs3AtkED71tcPexZvZi+HlvhHeLniII1msAf3L358qrjkROlwJcqTT60j51ZvZHgluadQiGJdztOnmPSwGuiEjyUoArIiIiItVKdXzwQEREJGmZ2W0Ez2FE+9Td74xHeUTiQT24IiIiIlKt6FcURERERKRaUYArIiIiItWKAlwRERERqVYU4IqIiIhItfL/Acg+lEITA0XFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Visualizing correlation\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.heatmap(correlation,annot=True,cmap='crest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the end of the lab!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to top](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "4. Exploratory Data Analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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